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The performance of Whatman 3-MM filter papers for the collection, drying, shipment and long-term storage of blood at ambient temperature, and for the detection of African swine fever virus and antibodies was assessed. Conventional and real-time PCR, viral isolation and antibody detection by ELISA were performed on paired samples (blood/tissue versus dried-blood 3-MM filter papers) collected from experimentally infected pigs and from farm pigs in Madagascar and Côte d'Ivoire. 3-MM filter papers were used directly in the conventional and real-time PCR without previous extraction of nucleic acids. Tests that performed better with 3-MM filter papers were in descending order: virus isolation, real-time UPL PCR and conventional PCR. The analytical sensitivity of real-time UPL PCR on filter papers was similar to conventional testing (virus isolation or conventional PCR) on organs or blood. In addition, blood-dried filter papers were tested in ELISA for antibody detection and the observed sensitivity was very close to conventional detection on serum samples and gave comparable results. Filter papers were stored up to 9 months at 20-25°C and for 2 months at 37°C without significant loss of sensitivity for virus genome detection. All tests on 3-MM filter papers had 100% specificity compared to the gold standards. Whatman 3-MM filter papers have the advantage of being cheap and of preserving virus viability for future virus isolation and characterization. In this study, Whatman 3-MM filter papers proved to be a suitable support for the collection, storage and use of blood in remote areas of tropical countries without the need for a cold chain and thus provide new possibilities for antibody testing and virus isolation. [ABSTRACT FROM AUTHOR]

Demonstration of successful elimination of lymphatic filariasis (LF) in endemic countries requires sensitive diagnostics for accurate definitions of endpoints and future surveillance. There has been interest in complementing available diagnostics with antibody serology testing in children, since negative serology would correspond with cessation of LF transmission. The Filariasis CELISA detects antifilarial IgG4 and has favourable results with serum samples but field application requires an easier sampling method. Ninety-four paired plasma and filter paper samples were assayed with promising results. The filter paper method resultedin a sensitivity of 92% and a specificity of 77% when compared to the paired plasma. One hundred and one filter paper samples were assessed for storage effects. Following 10-month storage at -20°C there was a significant reduction in reactivity (P < .001). Overall the results indicated that filter paper sampling would be a favourable sensitive and specific alternative for blood collection in surveys. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common and deadly type of cancer in the world. Based on machine learning algorithms such as XGBoost, random forest, logistic regression, and K-nearest neighbor, this paper establishes different models to classify and predict breast cancer, so as to provide a reference for the early diagnosis of breast cancer. Recall indicates the probability of detecting malignant cancer cells in medical diagnosis, which is of great significance for the classification of breast cancer, so this article takes recall as the primary evaluation index and considers the precision, accuracy, and F1-score evaluation indicators to evaluate and compare the prediction effect of each model. In order to eliminate the influence of different dimensional concepts on the effect of the model, the data are standardized. In order to find the optimal subset and improve the accuracy of the model, 15 features were screened out as input to the model through the Pearson correlation test. The K-nearest neighbor model uses the cross-validation method to select the optimal k value by using recall as an evaluation index. For the problem of positive and negative sample imbalance, the hierarchical sampling method is used to extract the training set and test set proportionally according to different categories. The experimental results show that under different dataset division (8 : 2 and 7 : 3), the prediction effect of the same model will have different changes. Comparative analysis shows that the XGBoost model established in this paper (which divides the training set and test set by 8 : 2) has better effects, and its recall, precision, accuracy, and F1-score are 1.00, 0.960, 0.974, and 0.980, respectively. [ABSTRACT FROM AUTHOR]

Background. Colorectal cancer (CRC) is one of the most common cancers in the world, identification of biomarkers for early detection of CRC represents a relevant target. The present study aims to determine serum peptidome patterns for CRC diagnosis. Methods. The present work focused on serum proteomic analysis of 32 health volunteers and 38 CRC by ClinProt Kit combined with mass spectrometry. This approach allowed the construction of a peptide patterns able to differentiate the studied populations. An independent group of serum (including 33 health volunteers, 34 CRC, 16 colorectal adenoma, 36 esophageal carcinoma, and 31 gastric carcinoma samples) was used to verify the diagnostic and differential diagnostic capability of the peptidome patterns blindly. An immunoassaymethod was used to determine serumCEA of CRC and controls. Results. A quick classifier algorithm was used to construct the peptidome patterns for identification of CRC from controls. Two of the identified peaks at m/z 741 and 7772 were used to construct peptidome patterns, achieving an accuracy close to 100% (>CEA, P < 0.05). Furthermore, the peptidome patterns could differentiate validation group with high accuracy. Conclusions. These results suggest that the ClinProt Kit combined with mass spectrometry yields significantly higher accuracy for the diagnosis and differential diagnosis of CRC. [ABSTRACT FROM AUTHOR]

The National Health and Family Planning Commission requires medical institutions to use the International Classification of Diseases (ICD) codes. However, due to many commonly used words in clinical disease descriptions, the direct mapping matching rate between the diagnosis names entered in the electronic medical records and the ICD codes is low. In this paper, based on the actual diagnostic data on the regional health platform, a disease term map incorporating standard terms was constructed. Specifically, based on the rule algorithm based on the components of the disease, a data-enhanced BERT (bidirectional encoder representation from transformers) upper and lower relationship recognition algorithm is proposed. Synonymous upper and lower relationships identify diseases, and the hierarchical structure is further integrated. In addition, a task assignment based on the association map of disease departments is also proposed. Methods were used for manual verification, and finally, 94,478 disease entities formed a large-scale disease term map, including 1,460 synonymous relationships and 46,508 hyponymous relationships. Evaluation experiments show that, based on the disease term map and clinical diagnosis, the coverage rate of diagnostic data is 75.31% higher than direct mapping coding based on ICD. In addition, using the disease term map to code diseases automatically will shorten the coding time by about 59.75% compared with manual coding by doctors, and the correct rate is 85%. [ABSTRACT FROM AUTHOR]

Traditional Chinese medicine (TCM) is a summary of the diagnosis and treatment experience formed by the working people in the long-term struggle against diseases, so it is very important to protect the intangible cultural heritage of TCM. How to extract valuable knowledge accurately and conveniently from the massive medical records of TCM is one of the important issues in the current research on the development of TCM. Due to the large amount of data of TCM medical records, many feature attributes, and diverse patterns, the existing classification technology has high computational complexity, low mining efficiency, and poor universality. Therefore, this paper proposed to quantify the medical records of TCM and obtained the main symptoms according to the improved hierarchical clustering feature selection algorithm. This paper also proposed a support vector machine (SVM) classification method using improved particle swarm algorithm to classify TCM information, which not only improves the efficiency and accuracy of TCM information classification but also discovers the potential dialectical and symptom patterns in diagnosis and treatment, so that the intangible cultural heritage protection of TCM can be developed sustainably. This paper showed that the information acquisition accuracy of the improved algorithm was very high. Before the improved algorithm was used, the accuracy of information mining for TCM was 67.90% at the highest and 65.53% at the lowest, but after using the improved algorithm, the accuracy rate of information mining for TCM was 88.02% at the highest and 82.45% at the lowest. It can be seen that using the improved algorithm to mine TCM information can quickly process effective information. [ABSTRACT FROM AUTHOR]

In recent years, due to the increase in the incidence of traffic accidents, the number of people with limb injuries has also increased. At the same time, among the aging population, neurological diseases or cardiovascular and cerebrovascular diseases have caused many people to have limb hemiplegia. It has been clinically proven that the use of rehabilitation equipment can help patients with limb injuries to restore limb motor function. This paper takes wearable lower limb rehabilitation exoskeleton as the research object, and its main contents are mechanical structure design, kinematics analysis, gait planning, virtual prototype simulation, and experimental verification and analysis. Based on the physiological characteristics of human body and the principle of comfortable and reliable wearing, this paper designs wearable exoskeleton for lower limb rehabilitation. Firstly, the physiological structure characteristics and movement mechanism of human lower limbs were studied and analyzed. By referring to the rotation range and height and size of each joint of human lower limbs, the overall scheme of wearable lower limb rehabilitation exoskeleton was designed and the degree of freedom was allocated. At the same time, Solidworks was used to establish a three-dimensional model. On the basis of a 3D model, a kinematics model was established, and the forward kinematics solution was obtained by using homogeneous coordinate transformation. Since the inverse kinematics solution was relatively complicated, the inverse kinematics solution was conducted in this paper according to the geometric relations of the joints of the lower limbs. Kinematics analysis of the exoskeleton structure of wearable lower limb rehabilitation was carried out to lay a theoretical foundation for gait planning. The off-line gait planning was carried out by using the method based on ZMP stability criterion, and the gait planning was divided into five stages: squat, start, middle step, stop and rise, and the motion trajectory of the center of mass and ankle joint was planned. Based on the inverse kinematics formula, the function of the change of joint angle with time in walking process is derived. The virtual prototype is established in ADAMS, and the simulation of virtual prototype is carried out by using the function of gait planning's joint angle and time. The correctness of structural design and gait planning was verified by measuring the trajectories of the centroid and ankle joints in each gait stage and the functional relationship between the rotation angle of each joint and time. Then, using a 3D dynamic capture system to capture the human lower limb motion trajectory of each joint, each joint trajectory data output, using the MATLAB software to output data, gets the joint trajectory change over time function curve and is used to verify feasibility and applicability of human gait planning. Through the research and analysis of the joints of the lower limbs of the human body, it can be concluded that the hip joint and the knee joint have 3 degrees of freedom, respectively, and the knee joint has 1 degree of freedom. [ABSTRACT FROM AUTHOR]

Nowadays, the implementation of Artificial Intelligence (AI) in medical diagnosis has attracted major attention within both the academic literature and industrial sector. AI would include deep learning (DL) models, where these models have been achieving a spectacular performance in healthcare applications. According to the World Health Organization (WHO), in 2020 there were around 25.6 million people who died from cardiovascular diseases (CVD). Thus, this paper aims to shad the light on cardiology since it is widely considered as one of the most important in medicine field. The paper develops an efficient DL model for automatic diagnosis of 12-lead electrocardiogram (ECG) signals with 27 classes, including 26 types of CVD and a normal sinus rhythm. The proposed model consists of Residual Neural Network (ResNet-50). An experimental work has been conducted using combined public databases from the USA, China, and Germany as a proof-of-concept. Simulation results of the proposed model have achieved an accuracy of 97.63% and a precision of 89.67%. The achieved results are validated against the actual values in the recent literature. [ABSTRACT FROM AUTHOR]

Polyorchidism, or supernumerary testis, is a rare congenital abnormality of the genitourinary system. In this paper, we present the case of triorchidism in a seven-year-old asymptomatic child with a suspect left scrotal mass detected on routine physical examination. Imaging studies revealed a third testicle in the left hemiscrotum, with comparable dimensions, signal intensity on MRI, and Doppler flow on ultrasound with the ipsilateral testis. We also discuss the clinical presentations, classifications, and current diagnostic and therapeutic strategies of this condition. [ABSTRACT FROM AUTHOR]

Based on the knowledge organization method, this paper explores the construction method of the traditional Chinese medicine (TCM) clinical knowledge coding model by taking TCM clinical electronic medical record data as the research object. Firstly, extracting technology is used to obtain the required data in the electronic medical record. Then, by constructing the clinical knowledge coding model, the tacit knowledge is made explicit, establishing the clinical knowledge base and exploring the connotation of TCM clinical knowledge. It provides necessary data resources for deepening the expression level of TCM clinical knowledge, constructing accurate TCM clinical diagnosis, intervention, and evaluation models, and promoting the inheritance, innovation, and development of TCM. In this paper, we extracted the data of 318 cases of distention and established the TCM clinical database from the basic information of patients, clinical diagnosis information, clinical diagnosis and treatment information, and clinical evaluation information. Based on the knowledge coding model and the connotation of knowledge attributes, the established TCM clinical knowledge base was to explore the law of TCM clinical precision diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Introduction. Cat scratch disease (CSD) is the most common cause of bacterial infectious lymphadenopathy, especially in children, but its diagnosis still remains challenging. Serological assays are widely applied due to their simplicity and the non-invasive sampling. However, these techniques present several limitations, including not well-defined antigen preparation, assay conditions and cutoff titers, severe cross-reactions with other species and organisms, and the notably ranging seroprevalence in the normal population. The objective of this study is to review the literature in order to determine the best diagnostic procedure for the diagnosis of CSD. Methods. Databases including PubMed, Medline, Google Scholar, and Google were searched to determine the best diagnostic procedure for the diagnosis of CSD. A total of 437 papers were identified and screened, and after exclusion of papers that did not fulfill the including criteria, 63 papers were used. Results. It was revealed that sensitivities of serological assays varied from 10% to 100%. Indeed, more than half of the studies reported a sensitivity lower than 70%, while 71% of them had a sensitivity lower than 80%. Moreover, specificities of serological assays ranged from 15% to 100%, with 25 assays reporting a specificity lower than 90%. Conclusion. It is considered that molecular assays should be the gold standard technique for CSD confirmation, and physicians are reinforced to proceed to lymph node biopsy in suspicious CSD cases. [ABSTRACT FROM AUTHOR]

Since two years ago, the COVID-19 virus has spread strongly in the world and has killed more than 6 million people directly and has affected the lives of more than 500 million people. Early diagnosis of the virus can help to break the chain of transmission and reduce the death rate. In most cases, the virus spreads in the infected person's chest. Therefore, the analysis of a chest CT scan is one of the most efficient methods for diagnosing a patient. Until now, various methods have been presented to diagnose COVID-19 disease in chest CT-scan images. Most recent studies have proposed deep learning-based methods. But handcrafted features provide acceptable results in some studies too. In this paper, an innovative approach is proposed based on the combination of low-level and deep features. First of all, local neighborhood difference patterns are performed to extract handcrafted texture features. Next, deep features are extracted using MobileNetV2. Finally, a two-level decision-making algorithm is performed to improve the detection rate especially when the proposed decisions based on the two different feature set are not the same. The proposed approach is evaluated on a collected dataset of chest CT scan images from June 1, 2021, to December 20, 2021, of 238 cases in two groups of patient and healthy in different COVID-19 variants. The results show that the combination of texture and deep features can provide better performance than using each feature set separately. Results demonstrate that the proposed approach provides higher accuracy in comparison with some state-of-the-art methods in this scope. [ABSTRACT FROM AUTHOR]

Ubiquitous computing and artificial intelligence are widely used in the field of Wise Information Technology of Med. It promotes the development of intelligent diagnosis and treatment, as well as reducing the workload of doctors' diagnosis and promoting the improvement of the diagnosis level of medical institutions in remote areas. The accurate detection of cervical cytopathy is related to the precise treatment and rehabilitation of patients. However, the low rate of accuracy in the existing cervical cytopathy image detection cannot satisfy the application needs of intelligent diagnosis. In this paper, a dual path network efficient detection method for cervical cytopathy (the proposed DSRNet50) is proposed, which is based on the deep learning method, and combines residual structure and dense connection. The proposed DSRNet50 is mainly based on residual structure, supplemented by dense connection paths, which improves the utilization of features, and maintains the ability of exploring new features by reducing feature redundancy. Meanwhile, the proposed DSRNet50 leverages packet convolution to reduce the computation burden of the network and eliminate the over fitting phenomenon of the network. The proposed DSRNet50 further exploits channel attention mechanism to recalibrate important features to suppress the propagation of irrelevant information. We use the Herlev dataset to verify the proposed DSRNet50 in the terms of the detection accuracy, parameter quantity, and computing complexity. The experiment results are presented to show the achievable performance of the proposed DSRNet50. [ABSTRACT FROM AUTHOR]

Timely disease detection and pest treatment are key issues in modern agricultural production, especially in large-scale crop agriculture. However, it is very time and effort-consuming to identify plant diseases manually. This paper proposes a deep learning model for agricultural crop disease identification based on AlexNet and Inception-V4. AlexNet and Inception-V4 are combined and modified to achieve an efficient but good performance. Experimental results on the expanded PlantVillage dataset show that the proposed model outperforms the compared methods: AlexNet, VGG11, Zenit, and VGG16, in terms of accuracy and F1 scores. The proposed model obtains the highest accuracy for corn, tomato, grape, and apple: 94.5%, 94.8%, 92.3%, and 96.5%, respectively. Also, the highest F1 scores for corn, tomato, grape, and apple: 0.938, 0.910, 0.945, and 0.924, respectively, are obtained. The results indicate that the proposed method has promising generalization ability in crop disease identification. [ABSTRACT FROM AUTHOR]

To realize the automatic symptom recognition and classification of MR images and improve the accuracy and efficiency of the diagnosis of lumbar intervertebral disc herniation (LDH), a method for lumbar intervertebral disc recognition and disease classification is proposed in this paper. The method mainly includes three steps: preprocessing, target segmentation, and symptom classification. Preprocessing is performed by noise reduction and interference removal methods for blurred images. The contour poles are used to determine the four points of the tail vertebra in order to reduce the wrong segmentation of the tail vertebra. A classification method based on five judgment indicators is proposed, which effectively improves the stability of disease diagnosis. The example verifies that the algorithm can accurately complete the target segmentation and the accuracy of symptom classification reaches the standard of professional doctors, which proves that the method has good robustness. [ABSTRACT FROM AUTHOR]

At present, artificial intelligence algorithms based on deep learning have achieved good results in image classification, biometric recognition, medical diagnosis, and other fields. However, in practice, many times researchers are unable to obtain a large number of samples due to many limitations or high sampling costs. Therefore, image sorting zero-sampling order research algorithms have become the central engine of intelligent processing and a hot spot for current research. Because of the need for the development of deep learning prediction capability, coupled with the emergence of time and technical-level drawbacks, the advantages of zero-sample and small-sample are gradually emerging, so this paper chooses to fuse the learning methods of both for image recognition research. This paper mainly introduces the current situation of zero-sample and small-sample learning and summarizes the learning of zero-sample and small-sample. And the meaning of zero-sample learning and small-sample learning and the classification of the main learning methods are introduced and compared and outlined, respectively. Finally, the methods of zero-sample and small-sample learning are fused, the design is introduced and analyzed, and the future research directions are prospected according to the current research problems. [ABSTRACT FROM AUTHOR]

Nanotechnology is an alternative material manufacturing technology, and the research field involves a broad field of modern technology, especially in the field of high-end manufacturing. Nanotechnology is a technique for studying the properties and applications of materials with structural dimensions in the range of 1 nanometer to 100 nanometers. Medical imaging refers to the technology and processing process of obtaining internal tissue images of the human body or a certain part of the human body in a noninvasive way for medical treatment or medical research; the acquired image can be further processed to restore the original image that was not clear enough to highlight some characteristic information in the image. The appearance of medical imaging has promoted the development of the medical field. The purpose of this paper is to study the application of nanoscale microparticle technology in medical imaging diagnosis and treatment. It is expected that the combination of nanotechnology and medical imaging will improve the accuracy of medical imaging diagnosis. This paper mainly introduces the general methods and technologies of technical development of medical imaging instruments and briefly shows the process of data and image analysis. The experimental results in this paper show that when other conditions remain unchanged, the image quality will change when the quality factor of the image changes. When the image quality is 60%, the image transmission rate is about 5.5FPS, which is relatively stable and can be used in the medical field. [ABSTRACT FROM AUTHOR]

The pathogenesis of depression is complex, and the current means of medical diagnosis is single. Patients with severe depression may even have great physical pain and suicidal tendencies. Magnetoencephalography (MEG) has the characteristics of ultrahigh spatiotemporal resolution and safety. It is a good medical means for the diagnosis of depression. In this paper, multivariate transfer entropy algorithm is used to study MEG of depression. In this paper, the subjects are divided into the same brain region and the multichannel combination between different brain regions, and the multivariate transfer entropy of patients with depression and healthy controls under different EEG signal frequency bands is calculated. Finally, the significant difference between the two groups of experimental samples is verified by the results of independent sample t-test. The experimental results show that for the same combination of brain channels, the multivariate transfer entropy in the depression group is generally lower than that in the healthy control group, and the difference is the best in γ frequency band and the largest in the frontal region. [ABSTRACT FROM AUTHOR]

This paper firstly reviews the achievements and existing problems of China's government in the supervision of medical service price in public hospitals in recent years. On this basis, patient's interests, the interests of the public hospital, medical staff perspective, analysis of the three main psychological cognitive differences' influence on medical diagnosis strategy, and the utility of public hospital medical service price regulation are analyzed, and accordingly perfect China's public hospital medical service price regulation suggestions are put forward. In this paper, taking a medical institution as an example, combined with the psychological cognitive differences of multi-interest theme, and through research, it is shown that the causes of influencing the unreasonable admission behavior of patients in medical institutions are as follows: first, the characteristics of vulnerable groups of patients affect the unreasonable admission behavior, mainly children and the elderly. Second, the health risk avoidance psychology drives patients to be hospitalized unreasonably, especially those with chronic diseases or those who pay attention to their own health. Third, the influence of convenience degree on unreasonable admission is relatively small. Fourth, doctor's advice has an important influence on unreasonable admission behavior. Fifth, the medical insurance system affects the unreasonable admission behavior of patients to a certain extent. [ABSTRACT FROM AUTHOR]

The Mahalanobis–Taguchi system (MTS) is a diagnostic and forecasting technique in a multidimensional system that integrates the Mahalanobis distance (MD) and robust engineering of Taguchi. To implement MTS, a set of observations from a normal group is selected to construct the Mahalanobis space (MS). With this MS as a reference, new observations from an unknown group can be judged to be normal or abnormal, and the degree of abnormality can be determined. MD is very sensitive to data changes, so the data quality of normal samples used to construct the MS directly affects the accuracy of classification. In practical applications, the selection of normal samples depends on the experience and subjective judgment of experts and lacks an objective selection mechanism. In this paper, a modified MD metric is proposed, which is combined with the individual control chart to obtain a robust MS. First, the initial MS is constructed according to the normal samples selected by experts, and the MD of each normal sample is calculated by the initial MS. Then, the MD of each normal sample in the corresponding reduced MS is computed, and the incremental MD is used as the new distance metric to establish the individual control charts. The stability rules of the control chart are employed to eliminate abnormal points, and the MS of the stable state is obtained. To evaluate the effectiveness of the modified MD metric, a numerical simulation experiment is implemented, and the results show that the proposed method is effective and improves the classification performance of MTS. Finally, the improved MTS method is applied to a real medical diagnosis case. [ABSTRACT FROM AUTHOR]

Skin disease is the major health problem around the world. The diagnosis of skin disease remains a challenge to dermatologist profession particularly in the detection, evaluation, and management. Health data are very large and complex due to this processing of data using traditional data processing techniques is very difficult. In this paper, to ease the complexity while processing the inputs, we use multilayered perceptron with backpropagation neural networks (MLP-BPNN). The image is collected from the devices that contain nanotechnology sensors, which is the state-of-art in the proposed model. The nanotechnology sensors sense the skin for its chemical, physical, and biological conditions with better detection specificity, sensitivity, and multiplexing ability to acquire the image for optimal classification. The MLP-BPNN technique is used to envisage the future result of disease type effectively. By using the above MLP-BPNN technique, it is easy to predict the skin diseases such as melanoma, nevus, psoriasis, and seborrheic keratosis. [ABSTRACT FROM AUTHOR]

Colorectal carcinoma (CRC) is commonly found in adults. CRC in the pediatric population is extremely rare. Usually, CRC is diagnosed in children at advanced stage due to a low clinical index of suspicion. Mucinous type of CRC and its signet ring variant are the most common histological types which carry very poor clinical outcomes. This paper reports a 17-year-old male who presented with mild pallor and a 3-month history of abdominal pain accompanied with a mass on the left lower quadrant, and it was then confirmed histologically to be mucinous CRC of signet ring variant. This paper will help to raise awareness among the physicians and pediatricians in including CRC in the preliminary workouts for the purpose of shortening the delay for diagnosis which in turn would compromise the prognosis of the patients. [ABSTRACT FROM AUTHOR]

Artificial intelligence has become one of the most rapidly developing disciplines in the application field of pattern recognition. In target recognition, sometimes, there are multiple identical or similar copies of the target to be recognized in the image, and it is difficult to classify and estimate by traditional methods. In this case, it is necessary to use the SOM network to separate multiple targets and use the multiple order parameters in the improved SNN to pair the target. The change of its thickness can intuitively reflect the abnormality of its tissue. Therefore, the choroidal thickness of the central fovea can be measured to study the relationship between the choroidal structure and BRVO and arteriosclerosis. The purpose of this study is to further study the correlation between branch retinal vein occlusion and arteriosclerosis by quantitatively measuring retinal vessel diameter and choroidal thickness, to analyze the correlation between different TCM syndrome types of nonischemic BRVO and retinal arteriosclerosis, and to provide theoretical basis for clinical nonischemic BRVO TCM syndrome types and traditional Chinese medicine treatment, so as to reflect its clinical application value. In order to solve the single fixed structure of traditional SNN and poor scalability, combined with the Kohonen layer structure in the self-organizing mapping network, an improved collaborative neural network model is proposed. This paper studies the network training method and operation convergence and analyzes the converged network and the pattern classification results obtained by the network. In order to solve the single fixed structure of traditional SNN and poor scalability, combined with the Kohonen layer structure in the self-organizing mapping network, an improved collaborative neural network model is proposed. The results of our proposed improved model on the MNIST dataset can achieve the same level of current state-of-the-art machine learning classifiers in recognition accuracy with a smaller network size and network complexity. [ABSTRACT FROM AUTHOR]

As a treasure of Chinese medicine, TCM has gradually formed and developed into a complete medicine with a unique medical theory system and rich treatment experience after thousands of years of medical practice. It requires high diagnostic experience, which is not conducive to application promotion and management. Therefore, the concept of digital medicine has been recognized by more and more people, in which medical diagnosis is one of the core issues of digital medicine. The accuracy and efficiency of medical diagnosis are closely related to people's life and health, which is an important problem that cannot be ignored. Use the growing case base as knowledge base to reason and realize the diagnosis function of traditional Chinese medicine. Based on the characteristics of traditional Chinese medicine and taking case reasoning as a model, an expert system of traditional Chinese medicine diagnosis is established. This paper combines the strong learning ability, strong adaptability, and large-scale parallel processing ability of artificial neural networks (ANN) to solve the nonlinear and unstructured complex problems in management information system. By improving BP algorithm to optimize the error of weight and repair or energy parameters, the overall error of the optimized system is reduced by about 75.3% after experimental analysis, and the average accuracy of prediction is 75%. [ABSTRACT FROM AUTHOR]

The artificial neural network reduces humanity and society's burden to solve complex problems highly efficiently. Artificial neural networks resemble brain activities based on the acquired training samples used for various applications such as classification, regression, prediction, smart grid, natural language processing, image processing, medical diagnosis, and so on. This paper illustrates the different artificial neural network architectures, types, merits, demerits, and applications. Therefore, this paper provides valuable information to students and researchers to enrich their knowledge about an artificial neural network and research it. This paper also proposed a multilayer-perceptron-neural-network-based solar irradiance forecasting model, an improved backpropagation neural network-based rainfall forecasting model, and an Elman neural network-based temperature forecasting model. The performances of the proposed neural network-based forecasting models are analyzed with various hidden neurons and validated using the acquired real-time meteorological data. The proposed neural network forecasting models achieve rigorous results with reduced errors for the considered applications and aid sustainability. [ABSTRACT FROM AUTHOR]

The article uses machine learning algorithms to extract disease symptom keyword vectors. At the same time, we used deep learning technology to design a disease symptom classification model. We apply this model to an online disease consultation recommendation system. The system integrates machine learning algorithms and knowledge graph technology to help patients conduct online consultations. The system analyses the misclassification data of different departments through high-frequency word analysis. The study found that the accuracy rate of our machine learning algorithm model to identify entities in electronic medical records reached 96.29%. This type of model can effectively screen out the most important pathogenic features. [ABSTRACT FROM AUTHOR]

Pulmonary fibrosis is a severe chronic lung disease that causes irreversible scarring in the tissues of the lungs, which results in the loss of lung capacity. The Forced Vital Capacity (FVC) of the patient is an interesting measure to investigate this disease to have the prognosis of the disease. This paper proposes a deep learning-based FVC-Net architecture to predict the progression of the disease from the patient's computed tomography (CT) scan and the patient's metadata. The input to the model combines the image score generated based on the degree of honeycombing for a patient identified based on segmented lung images and the metadata. This input is then fed to a 3-layer net to obtain the final output. The performance of the proposed FVC-Net model is compared with various contemporary state-of-the-art deep learning-based models, which are available on a cohort from the pulmonary fibrosis progression dataset. The model showcased significant improvement in the performance over other models for modified Laplace Log-Likelihood (−6.64). Finally, the paper concludes with some prospects to be explored in the proposed study. [ABSTRACT FROM AUTHOR]

In conventional healthcare, real-time monitoring of patient records and information mining for timely diagnosis of chronic diseases under certain health conditions is a crucial process. Chronic diseases, if not diagnosed in time, may result in patients' death. In modern medical and healthcare systems, Internet of Things (IoT) driven ecosystems use autonomous sensors to sense and track patients' medical conditions and suggest appropriate actions. In this paper, a novel IoT and machine learning (ML)-based hybrid approach is proposed that considers multiple perspectives for early detection and monitoring of 6 different chronic diseases such as COVID-19, pneumonia, diabetes, heart disease, brain tumor, and Alzheimer's. The results from multiple ML models are compared for accuracy, precision, recall, F1 score, and area under the curve (AUC) as a performance measure. The proposed approach is validated in the cloud-based environment using benchmark and real-world datasets. The statistical analyses on the datasets using ANOVA tests show that the accuracy results of different classifiers are significantly different. This will help the healthcare sector and doctors in the early diagnosis of chronic diseases. [ABSTRACT FROM AUTHOR]

A Boolean-valued information system (BIS) is an application of a soft set in which the data are mapped in a binary form and used in making applications not limited to decision-making, medical diagnoses, game theory, and economics. BIS may be lost for several reasons including virus attacks, improper entry, and machine errors. A concept was presented that the entire lost BIS can be regenerated from four aggregate sets through supposition. Based on that concept, this paper presents an algorithm to recalculate the entire BIS through a genetic algorithm (GA), named BISGA which is more general and easy to implement than the supposition method. A solved example is presented which explains how BISGA works. Furthermore, BISGA is implemented in Python and evaluated on both UCI benchmark datasets and randomized datasets for checking its efficiency and accuracy. Results show that the lost BIS is recovered significantly and accurately; however, the efficiency drops when the size of the BIS increases. This novel approach may help practitioners recalculate the entire lost BIS, which in turn helps in the decision-making process and conclusions. [ABSTRACT FROM AUTHOR]

Due to the recent technological revolution that is centered around information technology, the Internet of Medical Things (IoMT) has become an important research domain. IoMT is a combination of Internet of Things (IoT), big data, cloud computing, ubiquitous network, and three-dimensional holographic technology, which is used to build a smart medical diagnosis and treatment system. Additionally, this system should automate various activities, such as the patient's health record and health monitoring, which is an important issue in the development of modern and smart healthcare system. In this paper, we have thoroughly examined the role of a smart healthcare system architecture and other key supporting technologies in improving the health status of both indoor and outdoor patients. The proposed system has the capacity to investigate and predict (if feasible) the clinical application and nursing effects of totally implantable intravenous port (TIVAP) in pediatric hematological tumors. For this purpose, seventy children with hematologic tumors were treated with TIVAP, and IoMT-enabled care was provided to them, where the occurrence of adverse events, specifically after the treatment, was observed. The experimental results collected after the 70 children were treated and cared for by TIVAP show that there were five cases of adverse events, whereas the incidence rate of the adverse events was 7.14%. Moreover, TIVAP has significant efficacy in the treatment of hematologic tumors in children, and it equally reduces the vascular injury caused by chemotherapy in younger patients. Likewise, targeted care reduces the incidence of adverse events in children with expected ratio. [ABSTRACT FROM AUTHOR]

In recent years, as human life expectancy increases, birth rate decreases and health management concerns; the traditional Healthcare imaging system, with its uneven Healthcare imaging resources, high Healthcare imaging costs, and diagnoses often relying on doctors' clinical experience and equipment level limitations, has affected people's demand for health, so there is a need for a more accurate, convenient, and affordable Healthcare imaging system that allows all people to enjoy fair and quality Healthcare imaging services. This paper discusses the construction and evaluation of an intelligent medical diagnostic model based on integrated deep neural networks, which not only provides a systematic diagnostic analysis of the various symptoms input by the inquirer but also has higher accuracy and efficiency compared with traditional medical diagnostic models. The construction of this model provides a theoretical basis for integrating deep neural networks applied to medical neighborhoods with big data algorithms. [ABSTRACT FROM AUTHOR]

Usually, heart failure occurs when heart-related diseases are developed and continue to deteriorate veins and arteries. Heart failure is the final stage of heart disease, and it has become an important medical problem, particularly among the aging population. In medical diagnosis and treatment, the examination of heart failure contains various indicators such as electrocardiogram. It is one of the relatively common ways to collect heart failure or attack related information and is also used as a reference indicator for doctors. Electrocardiogram indicates the potential activity of patient's heart and directly reflects the changes in it. In this paper, a deep learning-based diagnosis system is presented for the early detection of heart failure particularly in elderly patients. For this purpose, we have used two datasets, Physio-Bank and MIMIC-III, which are publicly available, to extract ECG signals and thoroughly examine heart failure. Initially, a heart failure diagnosis model which is based on attention convolutional neural network (CBAM-CNN) is proposed to automatically extract features. Additionally, attention module adaptively learns the characteristics of local features and efficiently extracts the complex features of the ECG signal to perform classification diagnosis. To verify the exceptional performance of the proposed network model, various experiments were carried out in the realistic environment of hospitals. Influence of signal preprocessing on the performance of model is also discussed. These results show that the proposed CBAM-CNN model performance is better for both classifications of ECG signals. Likewise, the CBAM-CNN model is sensitive to noise, and its accuracy is effectively improved as soon as signal is refined. [ABSTRACT FROM AUTHOR]

Disease diagnosis faces challenges such as misdiagnosis, lack of diagnosis, and slow diagnosis. There are several machine learning techniques that have been applied to address these challenges, where a set of symptoms is applied to a classification model that predicts the presence or absence of a disease. To improve on the performance of these techniques, this paper presents a technique which involves feature selection using principal component analysis (PCA), a hybrid kernel-based support vector machine (HKSVM) classification model and hyperparameter optimization using genetic algorithm (GA). The HKSVM in this paper introduces a new way of combining three kernels: Radial basis function (RBF), linear, and polynomial. Combining local (RBF) and global (linear and polynomial) kernels has the effect of improved model performance. This is because the local kernels are better able to distinguish points closer to each other while the global kernels are more suited to distinguish points that are far away from each other. The PCA-GA-HKSVM is used on 7 different medical datasets, with two datasets being multiclass datasets and 5 datasets being binary. Performance evaluation metrics used were accuracy, precision, and recall. It was observed that the PCA-GA-HKSVM offered better performance than the single kernel support vector machines (SVMs). [ABSTRACT FROM AUTHOR]

Computer-aided diagnosis (CAD) has nearly fifty years of history and has assisted many clinicians in the diagnosis. With the development of technology, recently, researches use the deep learning method to get high accuracy results in the CAD system. With CAD, the computer output can be used as a second choice for radiologists and contribute to doctors doing the final right decisions. Chest abnormality detection is a classic detection and classification problem; researchers need to classify common thoracic lung diseases and localize critical findings. For the detection problem, there are two deep learning methods: one-stage method and two-stage method. In our paper, we introduce and analyze some representative model, such as RCNN, SSD, and YOLO series. In order to better solve the problem of chest abnormality detection, we proposed a new model based on YOLOv5 and ResNet50. YOLOv5 is the latest YOLO series, which is more flexible than the one-stage detection algorithms before. The function of YOLOv5 in our paper is to localize the abnormality region. On the other hand, we use ResNet, avoiding gradient explosion problems in deep learning for classification. And we filter the result we got from YOLOv5 and ResNet. If ResNet recognizes that the image is not abnormal, the YOLOv5 detection result is discarded. The dataset is collected via VinBigData's web-based platform, VinLab. We train our model on the dataset using Pytorch frame and use the mAP, precision, and F1-score as the metrics to evaluate our model's performance. In the progress of experiments, our method achieves superior performance over the other classical approaches on the same dataset. The experiments show that YOLOv5's mAP is 0.010, 0.020, 0.023 higher than those of YOLOv5, Fast RCNN, and EfficientDet. In addition, in the dimension of precision, our model also performs better than other models. The precision of our model is 0.512, which is 0.018, 0.027, 0.033 higher than YOLOv5, Fast RCNN, and EfficientDet. [ABSTRACT FROM AUTHOR]

Recently, many researchers have designed various automated diagnosis models using various supervised learning models. An early diagnosis of disease may control the death rate due to these diseases. In this paper, an efficient automated disease diagnosis model is designed using the machine learning models. In this paper, we have selected three critical diseases such as coronavirus, heart disease, and diabetes. In the proposed model, the data are entered into an android app, the analysis is then performed in a real-time database using a pretrained machine learning model which was trained on the same dataset and deployed in firebase, and finally, the disease detection result is shown in the android app. Logistic regression is used to carry out computation for prediction. Early detection can help in identifying the risk of coronavirus, heart disease, and diabetes. Comparative analysis indicates that the proposed model can help doctors to give timely medications for treatment. [ABSTRACT FROM AUTHOR]

The underground local fan and auxiliary fan also play a vital role in the underground air quality, compared with the system fan. However, the number of underground local fans and auxiliary fans is large and widely distributed, which is disadvantageous to adopt the same method of online monitoring and fault diagnosis method as the system fan. In order to find a new fault diagnosis method, which is cost-effective and reliable, this paper proposes a fault diagnosis method based on sound signal. It analyzes the source of fan noise and studies the overall scheme of mine fan fault diagnosis expert system based on sound signal. The fault expert system consists of four parts: signal acquisition and noise elimination, feature extraction, state recognition, and fault diagnosis. Its principle is briefly introduced. The denoising method of wavelet is adopted in this paper. Wavelet packet is used to extract the characteristics of sound signal, and the energy size and energy proportion of each frequency component are used as the basis of knowledge acquisition and reasoning. Through the analysis of the measured signals of the fan in the normal operating state, the feature vectors were extracted as the basis for the discrimination of the normal state after noise elimination. At the same time, the audio processing software was used to simulate the sound signals in three fault states. Then, the feature vector of the fault state is extracted, which is obviously different from that of the fan in the normal operation. As the basis of fault state analysis of the expert system, it lays the foundation for the realization of the expert system of mine fan equipment running state diagnosis. [ABSTRACT FROM AUTHOR]

English writing is conducive to the online communication and communication of language; the current diagnosis system of English writing is difficult to accurately find and diagnose the wrong words, which leads to a low diagnosis rate of wrong words in English writing system. To solve this problem, this paper designs an intelligent diagnosis system for English writing based on data feature extraction and fusion. First of all, B/S architecture is introduced on the basis of the conventional intelligent diagnosis system structure of English writing, which makes up for the problem that the C/S mode is prone to diagnostic errors. Secondly, the features of English lexical data are extracted and fused to provide better input for the diagnostic model, which effectively solves the problems of complex vocabulary and feature redundancy in English writing. The simulation results show that the proposed intelligent diagnosis system for English writing has higher diagnostic accuracy and faster query speed. [ABSTRACT FROM AUTHOR]

Machine learning is an expanding research area. Its main application is in the medical field and particularly the detection of epilepsy and epileptic seizures through electroencephalographic signals (EEG). It aims to design an intelligent framework that enables an immediate diagnosis of this disease without neurological consultation and thus saves the lives of the epileptic patients by detecting seizures and warning them before it happens. However, as a real-time application, this kind of framework faces several challenges such as accuracy, fast responses, and optimal memory usage. Within this context, our work was carried out. We propose a new machine learning framework based on chaos and fractal theories. Two main novelties are presented in this paper. Firstly, we propose a new method for signal preprocessing, and we reconstruct new versions of studied EEG signals using derivative determination and chaotic injection. Secondly, we suggest a new method for fractal analysis using Higuchi fractal dimension (HFD). In fact, HFDs extracted from EEG derivatives lead to detect epilepsy, whereas HFDs extracted from EEG with a chaotic signal injection lead to seizure detection. In addition, feature fusion helped to linearize all classification problems. An experimental study using the Bonn EEG database proves the efficiency of our contributions in comparison to published research. An accuracy of 100% was achieved in different classification cases using few features and a simple linear classifier. [ABSTRACT FROM AUTHOR]

People often concern the relationships between symptoms and diseases when seeking medical advices. In this paper, medical data are divided into three copies, records related to main disease categories, records related to subclass disease types, and records of specific diseases firstly; then two disease recognition methods only based on symptoms for the main disease category identification, subclass disease type identification, and specific disease identification are given. In the methods, a neural network and a support vector machine (SVM) algorithms are adopted, respectively. In the method validation part, accuracy of the two diagnosis methods is tested and compared. Results show that automatic disease prediction only based on symptoms is possible for intelligent medical triage and common disease diagnosis. [ABSTRACT FROM AUTHOR]

This paper discusses open-circuit fault (OCF) diagnosis and fault-tolerant control strategy (FTCS) of a nonisolated DC-DC converter. The photovoltaic power conversion structure (PPCS) consists of a photovoltaic generator and an interleaved Boost converter (IBC). The maximum power point tracking (MPPT) control of the IBC ensures operation at maximum power. The design of the nonlinear Backstepping control is detailed based on the equivalent average model of IBC, and the stability is studied using Lyapunov's theorem. The proposed OCF fault detection is based on sampling the voltage across the inductor at a much higher frequency than the switching frequency. In an OCF situation occurrence and a high control signal state, the detection of three negative samples is a condition for signaling the presence of an OCF fault; the photovoltaic system continues its normal operation. The simulation results show the validity of the proposed FTCS. The proposed diagnosis and control strategy improves the performance of the IBC in terms of cost, reliability, and service continuity. [ABSTRACT FROM AUTHOR]

Fermatean fuzzy sets (FFSs) have piqued the interest of researchers in a wide range of domains. The striking framework of the FFS is keen to provide the larger preference domain for the modeling of ambiguous information deploying the degrees of membership and nonmembership. Furthermore, FFSs prevail over the theories of intuitionistic fuzzy sets and Pythagorean fuzzy sets owing to their broader space, adjustable parameter, flexible structure, and influential design. The information measures, being a significant part of the literature, are crucial and beneficial tools that are widely applied in decision-making, data mining, medical diagnosis, and pattern recognition. This paper aims to expand the literature on FFSs by proposing many innovative Fermatean fuzzy sets-based information measures, namely, distance measure, similarity measure, entropy measure, and inclusion measure. We investigate the relationship between distance, similarity, entropy, and inclusion measures for FFSs. Another achievement of this research is to establish a systematic transformation of information measures (distance measure, similarity measure, entropy measure, and inclusion measure) for the FFSs. To accomplish this aim, new formulae for information measures of FFSs have been presented. To demonstrate the validity of the measures, we employ them in pattern recognition, building materials, and medical diagnosis. Additionally, a comparison between traditional and novel similarity measures is described in terms of counter-intuitive cases. The findings demonstrate that the innovative information measures do not include any absurd cases. [ABSTRACT FROM AUTHOR]

In recent years, brain magnetic resonance imaging (MRI) image segmentation has drawn considerable attention. MRI image segmentation result provides a basis for medical diagnosis. The segmentation result influences the clinical treatment directly. Nevertheless, MRI images have shortcomings such as noise and the inhomogeneity of grayscale. The performance of traditional segmentation algorithms still needs further improvement. In this paper, we propose a novel brain MRI image segmentation algorithm based on fuzzy C-means (FCM) clustering algorithm to improve the segmentation accuracy. First, we introduce multitask learning strategy into FCM to extract public information among different segmentation tasks. It combines the advantages of the two algorithms. The algorithm enables to utilize both public information among different tasks and individual information within tasks. Then, we design an adaptive task weight learning mechanism, and a weighted multitask fuzzy C-means (WMT-FCM) clustering algorithm is proposed. Under the adaptive task weight learning mechanism, each task obtains the optimal weight and achieves better clustering performance. Simulated MRI images from McConnell BrainWeb have been used to evaluate the proposed algorithm. Experimental results demonstrate that the proposed method provides more accurate and stable segmentation results than its competitors on the MRI images with various noise and intensity inhomogeneity. [ABSTRACT FROM AUTHOR]

One of the leading causes of female infertility is PCOS, which is a hormonal disorder affecting women of childbearing age. The common symptoms of PCOS include increased acne, irregular period, increase in body hair, and overweight. Early diagnosis of PCOS is essential to manage the symptoms and reduce the associated health risks. Nonetheless, the diagnosis is based on Rotterdam criteria, including a high level of androgen hormones, ovulation failure, and polycystic ovaries on the ultrasound image (PCOM). At present, doctors and radiologists manually perform PCOM detection using ovary ultrasound by counting the number of follicles and determining their volume in the ovaries, which is one of the challenging PCOS diagnostic criteria. Moreover, such physicians require more tests and checks for biochemical/clinical signs in addition to the patient's symptoms in order to decide the PCOS diagnosis. Furthermore, clinicians do not utilize a single diagnostic test or specific method to examine patients. This paper introduces the data set that includes the ultrasound image of the ovary with clinical data related to the patient that has been classified as PCOS and non-PCOS. Next, we proposed a deep learning model that can diagnose the PCOM based on the ultrasound image, which achieved 84.81% accuracy using the Inception model. Then, we proposed a fusion model that includes the ultrasound image with clinical data to diagnose the patient if they have PCOS or not. The best model that has been developed achieved 82.46% accuracy by extracting the image features using MobileNet architecture and combine with clinical features. [ABSTRACT FROM AUTHOR]

This paper discusses the imaging diagnostic features of arteriosclerotic encephalopathy and combines the spatial context information of local features to study the clinical imaging image copy detection algorithm. Moreover, this paper proposes a clinical imaging copy detection algorithm that combines the BOW model and spatial context embedding and a clinical imaging copy detection algorithm that combines the BOW model and global context verification. In addition, this paper applies the algorithm to the imaging diagnostic features of arteriosclerotic encephalopathy and sets up a controlled experiment to start research. The experimental research shows that the application of imaging diagnosis to the detection of subcortical arteriosclerotic encephalopathy has good clinical effects and rapid remission of patients' symptoms. The effectiveness of this method can be verified by a large number of clinical practices in follow-up studies. [ABSTRACT FROM AUTHOR]

The introduction of digital technology in the healthcare industry is marked by ongoing difficulties with implementation and use. Slow progress has been made in unifying different healthcare systems, and much of the globe still lacks a fully integrated healthcare system. As a result, it is critical and advantageous for healthcare providers to comprehend the fundamental ideas of AI in order to design and deliver their own AI-powered technology. AI is commonly defined as the capacity of machines to mimic human cognitive functions. It can tackle jobs with equivalent or superior performance to humans by combining computer science, algorithms, machine learning, and data science. The healthcare system is a dynamic and evolving environment, and medical experts are constantly confronted with new issues, shifting duties, and frequent interruptions. Because of this variation, illness diagnosis frequently becomes a secondary concern for healthcare professionals. Furthermore, clinical interpretation of medical information is a cognitively demanding endeavor. This applies not just to seasoned experts, but also to individuals with varying or limited skills, such as young assistant doctors. In this paper, we proposed the comparative analysis of various state-of-the-art methods of deep learning for medical imaging diagnosis and evaluated various important characteristics. The methodology is to evaluate various important factors such as interpretability, visualization, semantic data, and quantification of logical relationships in medical data. Furthermore, the glaucoma diagnosis system is discussed in detail via qualitative and quantitative approaches. Finally, the applications and future prospects were also discussed. [ABSTRACT FROM AUTHOR]

Pythagorean fuzzy sets are an extension of intuitionistic fuzzy sets and are more efficient from an application perspective. Though the Pythagorean fuzzy sets are more informative, not much work on similarity measures is available in the literature. Furthermore, existing similarity measures are not efficient. Also, the containment property in Pythagorean fuzzy units is not correctly defined or ineffective. As a result, the existing similarity measures do not reflect appropriate information between the Pythagorean fuzzy sets. The scalar function of the Yager is mainly used for verifying the validity of similarity measures. Most of the existing similarity measures do not conform to the Yager scalar function. Hence, the existing similarity measures exhibit some discrepancies. Furthermore, the existing similarity measures are inconsistent in determining the similarity in intuitionsitic and Pythagorean fuzzy sets. In some real‐world modeling issues, past, present, and cross‐time information are essential. However, such information is missing in the existing similarity measures. Therefore, in this paper, two new measures of similarity are being developed based on the deviation of the parameters: membership degree, nonmembership degree, strength of commitment, direction of commitment, and cross‐time evaluation factors. Under this construction, the proposed similarity measures effectively measure the similarity between the Pythagorean fuzzy sets. Furthermore, the newly defined containment property is also reflected in the proposed similarity measures, which were a limitation in most cases. Moreover, Yager's scalar function is also reflected by the proposed similarity measures. The complement of given information is also essential in some real‐world problems. However, such information is incomplete in the theory of Pythagorean fuzzy sets. Hence, the complement of the Pythagorean fuzzy set is being redefined, and a few related results on similarity measures are proposed. Finally, the proposed similarity measures are tested for applicability to medical diagnosis and clustering problems through some hypothetical case studies. [ABSTRACT FROM AUTHOR]

Women in low‐ and middle‐income countries where the prevalence and mortality of breast cancer are growing rapidly are more likely to be diagnosed at advanced stages, which negatively affects their treatment outcomes and chance of survival. The current literature in those settings tends to focus largely on explaining patient delay in seeking medical attention for breast symptoms. Meanwhile, little is known as to what prompts women to attend screening and diagnostic services after discovering symptomatic breasts. Drawn upon the data from in‐depth interviews with 33 breast cancer patients in Central Vietnam conducted in 2019, this paper examines the context of women's decisions about breast screening and how the practice of seeking cancer diagnosis occurred. Our findings reveal an absence of a national screening program and that seeking medical advice was conducted on an ad hoc basis after self‐detection of breast symptoms. Women's interpretations of symptomatic breasts as suspicious signs of cancer, the co‐occurrence of important life events, or encouragement by people in their social network motivated women to seek medical attention at different public and private health facilities. Their encounters with the health sector often involved multiple visits across time and space in which they experienced various forms of diagnosis delay produced by the health system. Our study carries implications for interventions to encourage women's awareness of early cancer symptoms and prompt medical presentation after self‐discovery of symptomatic breasts. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud computing and machine learning, using outsourced stored data and machine learning model for training and online-aided disease diagnosis has a great application prospect. However, training and diagnosis in an outsourced environment will cause serious challenges to the privacy of data. At present, many scholars have proposed privacy preserving machine learning schemes and made a lot of progress, but there are still great challenges in security and low client load. In this paper, we propose a complete privacy preserving outsourced multiclass SVM training and aided disease diagnosis scheme. We design some efficient basic operation algorithms for encrypted data. Then, we design an efficient and privacy preserving SVM model training protocol using the basic operation algorithms. We propose a secure maximum finding algorithm and secure comparison algorithm. Then, we design an efficient online-aided disease diagnosis scheme based on the BFV cryptosystem and blinding technique. Detailed security analysis proves that our scheme can protect the privacy of each participant. The experimental results illustrate that our proposed scheme significantly reduces the computation overhead compared with the previous similar works. Our proposed scheme completes most of the operations of aided disease diagnosis by the cloud servers and the client only needs to complete a small amount of encryption and decryption operations. The overall computation overhead is 0.175 s, and the efficiency of online aided disease diagnosis is improved by 85.4%. At the same time, our proposed scheme provides multiclass diagnosis results, which can better assist doctors in their treatment. [ABSTRACT FROM AUTHOR]

Diabetes is one of the biggest health problems that affect millions of people across the world. Uncontrolled diabetes can increase the risk of heart attack, cancer, kidney damage, blindness, and other illnesses. Researchers are motivated to create a Machine Learning methodology that can predict diabetes in the future. Exploiting Machine Learning Algorithms (MLA) is essential if healthcare professionals are able to identify diseases more effectively. In order to improve the medical diagnosis of diabetes this research explored and contrasts various MLA that can identify diabetes risk early. The research includes the analysis on real datasets such as a clinical dataset gathered from a doctor in the Indian district of Bandipora in the years April 2021–Feb2022. MLA are currently important in the healthcare sector due to their prediction abilities. Researchers are using MLA to improve disease prediction and reduce cost. In this Paper author developed a methodology using Machine Learning Algorithms for Diabetes Disease Risk Prediction in North Kashmir. Six MLA have been successfully used in the experimental study such as Random Forest (RF), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Gradient Boost (GB), Decision Tree (DT), and Logistic Regression (LR). RF is the most accurate classifier with the uppermost accuracy rate of 98 percent followed by MLP (90.99%), SVM (92%), GBC (97%), DT (96%), and LR (69%), respectively, with the balanced data set. Lastly, this study enables us to effectively identify the prevalence and prediction of diabetes. [ABSTRACT FROM AUTHOR]

Recently, end‐to‐end deep neural networks‐based glaucoma diagnosis approaches have been gaining much attention. However, the feature extractor and classier in these approaches are trained together, which is known as coadaptation. Therefore, the feature distribution in them should adapt to particular decision boundaries. To learn generic data representations and improve the generalization ability of the model, this paper designs a three‐stage framework for glaucoma diagnosis. In the first stage, preprocessing is utilized to extract the Region of Interesting around the Optic Disc to reduce the computational cost and nonobjective interference. In the second stage, Deep Sparse Autoencoder is designed to learn hybrid features between the deep features and the original features, which could improve the effectiveness of final high‐level feature expression. Meanwhile, L1 regularization is introduced and applied on the hybrid features to obtain deep features with high complementarity under small sample problem. In the third stage, the obtained generic feature representations are fed into different classifiers, in which Support Vector Machine classifier achieves the best diagnosis performance. The proposed approach is evaluated on two publicly available databases. Extensive experimental results indicate that our approach outperforms the state‐of‐the‐art approaches with the accuracy of 96.00%, 97.00% and Area Under Curve of 96.94%, 98.28% for REFUGE and Drishti‐GS1 databases, respectively. [ABSTRACT FROM AUTHOR]