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Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. The complex interplay of various risk factors highlights the urgent need for sophisticated analytical methods to more accurately predict stroke risks and manage their outcomes. Machine learning and deep learning technologies offer promising solutions by analyzing extensive datasets including patient demographics, health records, and lifestyle choices to uncover patterns and predictors not easily discernible by humans. These technologies enable advanced data processing, analysis, and fusion techniques for a comprehensive health assessment. We conducted a comprehensive review of 25 review papers published between 2020 and 2024 on machine learning and deep learning applications in brain stroke diagnosis, focusing on classification, segmentation, and object detection. Furthermore, all these reviews explore the performance evaluation and validation of advanced sensor systems in these areas, enhancing predictive health monitoring and personalized care recommendations. Moreover, we also provide a collection of the most relevant datasets used in brain stroke analysis. The selection of the papers was conducted according to PRISMA guidelines. Furthermore, this review critically examines each domain, identifies current challenges, and proposes future research directions, emphasizing the potential of AI methods in transforming health monitoring and patient care. [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]

Since reporting cases of breast cancer are on the rise all over the world. Especially in regions such as Pakistan, Saudi Arabia, and the United States. Efficient methods for the early detection and diagnosis of breast cancer are needed. The usual diagnosis procedures followed by physicians has been updated with modern diagnostic approaches that include computer-aided support for better accuracy. Machine learning based practices has increased the accuracy and efficiency of medical diagnosis, which has helped save lives of many patients. There is much research in the field of medical imaging diagnostics that can be applied to the variety of data such as magnetic resonance images (MRIs), mammograms, X-rays, ultrasounds, and histopathological images, but magnetic resonance (MR) and mammogram imaging have proved to present the promising results. The proposed paper has presented the results of classification algorithms over Breast Cancer (BC) mammograms from a novel dataset taken from hospitals in the Qassim health cluster of Saudi Arabia. This paper has developed a novel approach called the novel spectral extraction algorithm (NSEA) that uses feature extraction and fusion by using local binary pattern (LBP) and bilateral algorithms, as well as a support vector machine (SVM) as a classifier. The NSEA with the SVM classifier demonstrated a promising accuracy of 94% and an elapsed time of 0.68 milliseconds, which were significantly better results than those of comparative experiments from classifiers named Naïve Bayes, logistic regression, K-Nearest Neighbor (KNN), Gaussian Discriminant Analysis (GDA), AdaBoost and Extreme Learning Machine (ELM). ELM produced the comparative accuracy of 94% however has a lower elapsed time of 1.35 as compared to SVM. Adaboost has produced a fairly well accuracy of 82%, KNN has a low accuracy of 66%. However Logistic Regression, GDA and Naïve Bayes have produced the lowest accuracies of 47%, 43% and 42%. [ABSTRACT FROM AUTHOR]

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In order to improve the diagnosis and classification effect of pituitary tumors, this paper combines the current common machine learning methods and classification prediction methods to improve the traditional machine learning algorithms. Moreover, this paper analyzes the feature algorithm based on the feature extraction requirements of pituitary tumor pictures and compares a variety of commonly used algorithms to select a classification algorithm suitable for the model of this paper through comparison methods. In addition, this paper carries out the calculation of the prediction algorithm and verifies the algorithm according to the actual situation. Finally, based on the neural network algorithm, this paper designs and constructs the algorithm function module and combines the actual needs of pituitary tumors to build the model and verify the performance of the model. The research results show that the model system constructed in this paper meets the expected demand. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a neurodegenerative disorder that causes memory degradation and cognitive function impairment in elderly people. The irreversible and devastating cognitive decline brings large burdens on patients and society. So far, there is no effective treatment that can cure AD, but the process of early-stage AD can slow down. Early and accurate detection is critical for treatment. In recent years, deep-learning-based approaches have achieved great success in Alzheimer's disease diagnosis. The main objective of this paper is to review some popular conventional machine learning methods used for the classification and prediction of AD using Magnetic Resonance Imaging (MRI). The methods reviewed in this paper include support vector machine (SVM), random forest (RF), convolutional neural network (CNN), autoencoder, deep learning, and transformer. This paper also reviews pervasively used feature extractors and different types of input forms of convolutional neural network. At last, this review discusses challenges such as class imbalance and data leakage. It also discusses the trade-offs and suggestions about pre-processing techniques, deep learning, conventional machine learning methods, new techniques, and input type selection. [ABSTRACT FROM AUTHOR]

Simple Summary: The amount of diagnosed thyroid nodules increases every year. Many researchers have tried to optimize the process of classifying and diagnosing thyroid nodules using artificial intelligence. The aim of this study was to assess the latest applications of artificial intelligence in diagnosing and classifying thyroid nodules. The focus was on innovations in the use of artificial intelligence in the field of ultrasonography and microscopic diagnosis, although other applications were reviewed as well. In total, we analyzed 930 papers published from 2018 to 2022. The incidence of thyroid nodules diagnosed is increasing every year, leading to a greater risk of unnecessary procedures being performed or wrong diagnoses being made. In our paper, we present the latest knowledge on the use of artificial intelligence in diagnosing and classifying thyroid nodules. We particularly focus on the usefulness of artificial intelligence in ultrasonography for the diagnosis and characterization of pathology, as these are the two most developed fields. In our search of the latest innovations, we reviewed only the latest publications of specific types published from 2018 to 2022. We analyzed 930 papers in total, from which we selected 33 that were the most relevant to the topic of our work. In conclusion, there is great scope for the use of artificial intelligence in future thyroid nodule classification and diagnosis. In addition to the most typical uses of artificial intelligence in cancer differentiation, we identified several other novel applications of artificial intelligence during our review. [ABSTRACT FROM AUTHOR]

Objective: Skin diseases constitute a widespread health concern, and the application of machine learning and deep learning algorithms has been instrumental in improving diagnostic accuracy and treatment effectiveness. This paper aims to provide a comprehensive review of the existing research on the utilization of machine learning and deep learning in the field of skin disease diagnosis, with a particular focus on recent widely used methods of deep learning. The present challenges and constraints were also analyzed and possible solutions were proposed. Methods: We collected comprehensive works from the literature, sourced from distinguished databases including IEEE, Springer, Web of Science, and PubMed, with a particular emphasis on the most recent 5-year advancements. From the extensive corpus of available research, twenty-nine articles relevant to the segmentation of dermatological images and forty-five articles about the classification of dermatological images were incorporated into this review. These articles were systematically categorized into two classes based on the computational algorithms utilized: traditional machine learning algorithms and deep learning algorithms. An in-depth comparative analysis was carried out, based on the employed methodologies and their corresponding outcomes. Conclusions: Present outcomes of research highlight the enhanced effectiveness of deep learning methods over traditional machine learning techniques in the field of dermatological diagnosis. Nevertheless, there remains significant scope for improvement, especially in improving the accuracy of algorithms. The challenges associated with the availability of diverse datasets, the generalizability of segmentation and classification models, and the interpretability of models also continue to be pressing issues. Moreover, the focus of future research should be appropriately shifted. A significant amount of existing research is primarily focused on melanoma, and consequently there is a need to broaden the field of pigmented dermatology research in the future. These insights not only emphasize the potential of deep learning in dermatological diagnosis but also highlight directions that should be focused on. [ABSTRACT FROM AUTHOR]

Big data in medical diagnosis can provide abundant value for clinical diagnosis, decision support and many other applications, but obtaining a large number of labeled medical data will take a lot of time and manpower. In this paper, a classification model based on semi-supervised learning algorithm using both labeled and unlabeled data is proposed to process big data in medical diagnosis, which includes structured, semi-structured and unstructured data. For the medical laboratory data, this paper proposes a self-training algorithm based on repeated labeling strategy to solve the problem that mislabeled samples weaken the performance of classifiers. Aiming at medical record data, this paper extracts features with high correlation of classification results based on domain expert knowledge base first, and then chooses the unlabeled medical record data with the highest confidence to expand the training set and optimizes the performance of the classifiers of tri-training algorithm, which uses supervised learning algorithm to train three basic classifiers. The experimental results show that the proposed medical diagnosis data classification model based on semi-supervised learning algorithm has good performance. [ABSTRACT FROM AUTHOR]

This paper focuses on developing a regularization-based feature selection approach to select the most effective attributes from the Parkinson's speech dataset. Parkinson's disease is a medical condition that progresses as the dopamine-producing nerve cells are affected. Early diagnosis often reduces the effect on the individuals, minimizes the advancement over time. In recent times, intelligent computational models are used in many complex cases to diagnose a clinical condition with high precision. These models are intended to find meaningful representation from the data to diagnose the disease. Machine learning acts as a tool, gears up the model learning process through a mathematical baseline. But, not in all cases, machine learning will be demanded to perform optimally. It comes with a few constraints, mainly the representation of the data. The learning models expect a clean, noise-free input, which in-turns produces better discriminative patterns over different categories of classes. The proposed model identified five candidate features as predictors. This feature subset is trained with different varieties of supervised classifiers to trace out the best-performing model. The results are validated through accuracy, precision, recall, and receiver's operational characteristic curves. The proposed regularization- based feature selection model outperformed the benchmark algorithms by attaining 100% accuracy on most of the classifiers, other than linear discriminant analysis (99.90%) and naïve Bayes (99.51%). This paper exhibits the need for intelligent models to analyze complex data patterns to assist medical practitioners in better disease diagnosis. The results exhibit that the regularization methods find the best features based on their importance score, which improved the model performance over other feature selection methods. [ABSTRACT FROM AUTHOR]

This paper promotes better life quality and lifestyle for patients. We attain this goal by creating a mobile application that analyses patient's medical records, such as diabetes, hypertension, and chronic kidney diseases. Then, we implement the system to diagnose patients with chronic conditions using machine learning techniques. Machine learning classifiers are used in this paper to decide whether a person has any chronic diseases. The investigated diseases are hypertension, diabetes, and chronic kidney disease. Four datasets were used to build the classifying models. Orange3 from Anaconda-Navigator, a data mining tool, was used to test machine learning algorithms. The study findings revealed the superiority of the tree algorithm with 100% accuracy for hypertension; it was the highest outcome for both males and females using Orange3. The highest precision, which is 100%, is observed by SVM, k-NN, decision trees, logistic regression, and CART for hypertension males' data collection. In comparison, the highest precision is 100% in SVM, MLP, decision tree, random forest, logistic regression, and CART for the female dataset. We conclude that the two datasets for the same diseases share mostly the same algorithm accuracy. For kidneys, the Random Forest algorithm produced 100% accuracy, which is the highest value among other algorithms. For diabetes, neural networks have attested the best accuracy. It was 76.3%, yet the accuracy increased slightly as the kNN algorithm showed 83% accuracy. [ABSTRACT FROM AUTHOR]

Multiple sclerosis (MS) is one of the most common inflammatory neurological diseases in young adults. There are three types of MS: (1) In relapsing remitting MS (RRMS), people have temporarily periods of relapses (attacks) for days or weeks, and then symptoms seem to disappear (remitting stage). (2) In secondary progressive MS (SPMS), symptoms worsen more steadily over time. Attacks (relapses) may occur time to time but the disease can progress in non-attack periods. It is estimated that half of the RRMS patients progress to SPMS in 10 years. (3) Primary progressive MS (PPMS) is characterized by slowly worsening symptoms from the beginning, with no relapses or remissions. For PPMS patients, disability progresses slowly. Researchers have found out that in the first year, MS causes more damage than following 5–10 years. Therefore, early diagnosis is vital. In this context, deep learning models started to be popular for assisting identification/diagnosis/classification of MS patients using magnetic resonance imaging (MRI). This paper provides an in-depth review of deep learning approaches for identification and classification of MS using brain MRI images. We discuss recent trends of deep learning methods for MS identification under three categories: CNN models, hybrid models (CNN with a classifier) and deep transfer learning models. Existing deep learning algorithms are analyzed and compared according to their architecture, image modality, pre-processing, feature extraction, classifier, dataset, categories and accuracy. This survey paper would provide a valuable source for researchers who are interested in state-of-the-art deep learning methods for MS identification using MRI images. [ABSTRACT FROM AUTHOR]

The world's population is increasing and so is the challenge on existing healthcare infrastructure to cope with the growing demand in medical diagnosis and evaluation. Although human experts are primarily tasked with the diagnosis of different medical conditions, artificial intelligence (AI)-assisted diagnoses have become considerably useful in recent times. One of the critical lung infections, which requires early diagnosis and subsequent treatment to reduce the mortality rate, is pneumonia. There are different methods for obtaining a pneumonia diagnosis; however, the adoption of chest X-rays is popular since it is non-invasive. The AI systems for a pneumonia diagnosis using chest X-rays are often built on supervised machine-learning (ML) models, which require labeled datasets for development. However, collecting labeled datasets is sometimes infeasible due to constraints such as human resources, cost, and time. As such, the problem that we address in this paper is the unsupervised classification of pneumonia using unsupervised ML models including the beta-variational convolutional autoencoder (β-VCAE) and other variants, such as convolutional autoencoders (CAE), denoising convolutional autoencoders (DCAE), and sparse convolutional autoencoders (SCAE). Namely, the pneumonia classification problem is cast into an anomaly detection to develop the aforementioned ML models. The experimental results show that pneumonia can be diagnosed with high recall, precision, f1-score, and f2-score using the proposed unsupervised models. In addition, we observe that the proposed models are competitive with the state-of-the-art models, which are trained on a labeled dataset. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most prevalent cancers among women worldwide, and early detection of the disease can be lifesaving. Detecting breast cancer early allows for treatment to begin faster, increasing the chances of a successful outcome. Machine learning helps in the early detection of breast cancer even in places where there is no access to a specialist doctor. The rapid advancement of machine learning, and particularly deep learning, leads to an increase in the medical imaging community's interest in applying these techniques to improve the accuracy of cancer screening. Most of the data related to diseases is scarce. On the other hand, deep-learning models need much data to learn well. For this reason, the existing deep-learning models on medical images cannot work as well as other images. To overcome this limitation and improve breast cancer classification detection, inspired by two state-of-the-art deep networks, GoogLeNet and residual block, and developing several new features, this paper proposes a new deep model to classify breast cancer. Utilizing adopted granular computing, shortcut connection, two learnable activation functions instead of traditional activation functions, and an attention mechanism is expected to improve the accuracy of diagnosis and consequently decrease the load on doctors. Granular computing can improve diagnosis accuracy by capturing more detailed and fine-grained information about cancer images. The proposed model's superiority is demonstrated by comparing it to several state-of-the-art deep models and existing works using two case studies. The proposed model achieved an accuracy of 93% and 95% on ultrasound images and breast histopathology images, respectively. [ABSTRACT FROM AUTHOR]

Liver cancer has a huge negative impact on the human survival. However, the traditional histopathological diagnostic methods have a large burden on the clinical diagnosis due to the large workload required. To this end, by combining the machine learning method and the whole slide images (WSIs), this paper proposes a novelty method to improve the efficiency of the clinical diagnosis. The core of our proposed method is to use a patch-based convolutional neural network to perform the category prediction (normal or tumor) on the patches extracted from the 60 liver tumor WSIs. Specifically, we design a total of four sets of the comparative experiment for the patch classification in order to screen the classifier with the best classification effect. The best classifier is then used to predict the category of patches in the testing set, and the results are combined to generate a probability heatmap to assist clinical diagnosis in an intuitive way. The experimental comparisons verify the validity of the proposed model which can achieve a classification accuracy rate of 99.94% in the patch classification task. [ABSTRACT FROM AUTHOR]

The paper classifies whether or not a person has brain tumor using transfer learning approach. EfficientNetB0 and VGG16 models were employed because of their accuracy. The results indicate that the accuracy achieved by EfficientNetB0 was more than that of VGG16 model. Due to its high accuracy, transfer learning model can be used for decision-making purpose with respect to clinical scenarios. [ABSTRACT FROM AUTHOR]

Machine learning has proven to be one of the efficient solutions for analyzing complex data to perform identification and classification. With a large number of learning tools and techniques, the health section has significantly benefited from solving the diagnosis problems. This paper has reviewed some of the recent scientific implementations on learning-based schemes to find that existing studies of learning have mainly focused on predictive analysis with less emphasis on preprocessing and more inclination towards adopting sophisticated learning schemes that offer higher accuracy at the cost of the higher computational burden. Therefore, the proposed method addresses the concern mentioned above by a novel computational learning model that emphasizes fine-tuning complex medical data and makes it suitable for learning to balance better classification performance and computational complexity. The implementation is carried out using the MIMIC-III dataset, where the proposed system discretizes, the complete model using physician reports and furnished patient information as the first step. It also prepares the data by choosing a specific tuple and its associated field. The second step introduces a novel relatedness function where preprocessing is carried out using word quantization while adopting auto-encoders in deep learning followed by a novel learning-based diagnosis. The outcome exhibits that the proposed system offers better classification performance in reduced processing time in comparison to existing learning schemes. [ABSTRACT FROM AUTHOR]

Electric heartbeat was monitored with an electrocardiogram. It was an easy and fun way to diagnose heart problems but also to keep a healthy record of our bodies. Heart disease often manifests itself as chest tightness, sickness, light head edness, severe instability, chest tightness, rapid heartbeat, shortness of breath, fatigue, drowsiness, or even decreased exercise rate. The same basic principle would be for machines to be straight within it, but without the intervention of users, and also to change their habits accordingly. Distinguishing is among the machine-readable learning strategies they use to classify data sets effectively. This paper introduces a multidisciplinary survey in the separation of ECG data using machine learning methods found at the beginning of the literature. It is very useful in both computer and medical research in the field of automatic diagnosis of heart disease. Class dividers improve physician accuracy and early detection for better treatment of affected patients. [ABSTRACT FROM AUTHOR]

Diabetic foot is one of the main complications observed in diabetic patients; it is associated with the development of foot ulcers and can lead to amputation. In order to diagnose these complications, specialists have to analyze several factors. To aid their decisions and help prevent mistakes, the resort to computer-assisted diagnostic systems using artificial intelligence techniques is gradually increasing. In this paper, two different models for the classification of thermograms of the feet of diabetic and healthy individuals are proposed and compared. In order to detect and classify abnormal changes in the plantar temperature, machine learning algorithms are used in both models. In the first model, the foot thermograms are classified into four classes: healthy and three categories for diabetics. The second model has two stages: in the first stage, the foot is classified as belonging to a diabetic or healthy individual, while, in the second stage, a classification refinement is conducted, classifying diabetic foot into three classes of progressive severity. The results show that both proposed models proved to be efficient, allowing us to classify a foot thermogram as belonging to a healthy or diabetic individual, with the diabetic ones divided into three classes; however, when compared, Model 2 outperforms Model 1 and allows for a better performance classification concerning the healthy category and the first class of diabetic individuals. These results demonstrate that the proposed methodology can be a tool to aid medical diagnosis. [ABSTRACT FROM AUTHOR]

To address the problem of high feature dimensionality of Parkinson's disease medical data, this paper introduces SHapley Additive exPlanations (SHAP) value for feature selection of Parkinson's disease medical dataset. This paper combines SHAP value with four classifiers, namely deep forest (gcForest), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM) and random forest (RF), respectively. Then this paper applies them to Parkinson's disease diagnosis. First, the classifier is used to calculate the magnitude of contribution of SHAP value to the features, then the features with significant contribution in the classification task are selected, and then the data after feature selection is used as input to classify the Parkinson's disease dataset for diagnosis using the classifier. The experimental results show that compared to Fscore, analysis of variance (Anova-F) and mutual information (MI) feature selection methods, the four models based on SHAP-value feature selection achieved good classification results. The SHAP-gcForest model combined with gcForest achieves classification accuracy of 91.78% and F1-score of 0.945 when 150 features are selected. The SHAP-LightGBM model combined with LightGBM achieves classification accuracy and F1-score of 91.62% and 0.945 when 50 features are selected, respectively. The classification effectiveness is second only to the SHAP-gcForest model, but the SHAP-LightGBM model is more computationally efficient than the SHAP-gcForest model. Finally, the effectiveness of the proposed method is verified by comparing it with the results of existing literature. The findings demonstrate that machine learning with SHAP value feature selection method has good classification performance in the diagnosis of Parkinson's disease, and provides a reference for physicians in the diagnosis and prevention of Parkinson's disease. [ABSTRACT FROM AUTHOR]

Early diagnosis of autism is extremely beneficial for patients. Traditional diagnosis approaches have been unable to diagnose autism in a fast and accurate way; rather, there are multiple factors that can be related to identifying the autism disorder. The gene expression (GE) of individuals may be one of these factors, in addition to personal and behavioral characteristics (PBC). Machine learning (ML) based on PBC and GE data analytics emphasizes the need to develop accurate prediction models. The quality of prediction relies on the accuracy of the ML model. To improve the accuracy of prediction, optimized feature selection algorithms are applied to solve the high dimensionality problem of the datasets used. Comparing different optimized feature selection methods using bio-inspired algorithms over different types of data can allow for the most accurate model to be identified. Therefore, in this paper, we investigated enhancing the classification process of autism spectrum disorder using 16 proposed optimized ML models (GWO-NB, GWO-SVM, GWO-KNN, GWO-DT, FPA-NB, FPA-KNN, FPA-SVM, FPA-DT, BA-NB, BA-SVM, BA-KNN, BA-DT, ABC-NB, ABC-SVM, ABV-KNN, and ABC-DT). Four bio-inspired algorithms namely, Gray Wolf Optimization (GWO), Flower Pollination Algorithm (FPA), Bat Algorithms (BA), and Artificial Bee Colony (ABC), were employed for optimizing the wrapper feature selection method in order to select the most informative features and to increase the accuracy of the classification models. Five evaluation metrics were used to evaluate the performance of the proposed models: accuracy, F1 score, precision, recall, and area under the curve (AUC). The obtained results demonstrated that the proposed models achieved a good performance as expected, with accuracies of 99.66% and 99.34% obtained by the GWO-SVM model on the PBC and GE datasets, respectively. [ABSTRACT FROM AUTHOR]

Alzheimer's an irreversible neurodegenerative disease with the most far-reaching impact, the most extensive, and the most difficult to cure in the world. It is also the most common disease of Alzheimer's disease. With the rapid rise of data mining, machine learning and other fields, they have penetrated various disciplines. In particular, research in the field of AD is developing rapidly and has demonstrated strong vitality. In terms of data, Alzheimer's Disease Neuroimaging Initiative (ADNI) researchers collect, verify and use a variety of data modalities as predictors of disease, including MRI and PET images, genetics, cognitive testing, cerebrospinal fluid and blood biomarkers, etc. Therefore, this paper uses a multi-task learning algorithm based on the ADNI data set to implement regression tasks and predict the cognitive scores of subjects in the next 3 years. This method can effectively assess the cognitive trends of patients in the future and aims to predict the progression of the disease. In addition, we used four different machine learning classification algorithms to conduct fusion research on AD multi-modal data, including MRI, PET, and cognitive scoring data. This method can determine the current patient's cognitive stage, to achieve the effect of assisting doctors in diagnosis. Finally, we designed a multi-modal data platform technical architecture to standardize management and sharing of ADNI data and data obtained by offline medical institutions to improve the utilization and value of data. The design of the technical architecture proposed in this article is more easily scalable and compatible with other neurological diseases. Nowadays, the large amount of data being generated by AD can provide valuable solutions for the research of disease progression prediction and auxiliary diagnosis. [ABSTRACT FROM AUTHOR]

Recently, solving ordinal classification problems using machine learning and deep learning techniques has acquired important attention. There are many real-world problems in different areas of knowledge where a categorical variable needs to be predicted, and the existing categories follow an order associated with the nature of the problem: e.g. medical diagnosis with different states of a disease, or industrial quality assessment with different levels of quality. In these problems, it is quite common that the final label for each sample is determined by a group of experts with different opinions, and all opinions are usually summarised in a single crisp label by means of a given statistic (e.g. the median or the mode). Applying standard ordinal classifiers to these crisp labels could result in overfitting, as the labelling information is considered as totally certain. In this work, we propose a unimodal regularisation approach based on soft labelling, i.e. the ordinal information is used to introduce the inherent uncertainty of the label fusion. Specifically, said regularisation is based on using triangular distributions to simulate the aforementioned fusion of the expert opinions, where a parameter is used to decide the amount of probability that is assigned to the target category and the adjacent ones (according to the ordinal scale). The strategy could be applied to the loss function used by any ordinal classification learning algorithm, but we focus on deep learning in this paper. The proposal is compared to a baseline approach for nominal classification tasks and other state-of-the-art unimodal regularisation methods, and the experimental validation includes six benchmark datasets and five performance metrics. The results along with the statistical analysis show that the proposed methodology significantly outperforms the rest of the methods. • Unimodal regularised loss function based on triangular distributions. • Application to different CNN models and ordinal benchmark datasets. • Comparison with other regularisation methods based on different distributions. • Improvement of state-of-the-art classification performance regarding different metrics. [ABSTRACT FROM AUTHOR]

Arrhythmia detection and classification is a crucial step for diagnosing cardiovascular diseases. However, deep learning models that are commonly used and trained in end-to-end fashion are not able to provide good interpretability. In this paper, we address this deficiency by proposing the first novel interpretable arrhythmia classification approach based on a human-machine collaborative knowledge representation. Our approach first employs an AutoEncoder to encode electrocardiogram signals into two parts: hand-encoding knowledge and machine-encoding knowledge. A classifier then takes as input the encoded knowledge to classify arrhythmia heartbeats with or without human in the loop (HIL). Experiments and evaluation on the MIT-BIH Arrhythmia Database demonstrate that our new approach not only can effectively classify arrhythmia while offering interpretability, but also can improve the classification accuracy by adjusting the hand-encoding knowledge with our HIL mechanism. [ABSTRACT FROM AUTHOR]

EEG is one of the most useful techniques used to represent behaviours of the brain and helps explore valuable insights through the measurement of brain electrical activity. Hence, plays a vital role in detecting neurological conditions. In this paper, we identify some unique EEG patterns pertaining to dyslexia, which is a learning disability with a neurological origin. Although EEG signals hold important insights of brain behaviours, uncovering these insights are not always straightforward due to its complexity. We tackle this using machine learning and uncover unique EEG signals generated in adults with dyslexia during writing and typing as well as optimal EEG electrodes and brain regions for classification. This study revealed that the greater level of difficulties seen in individuals with dyslexia during writing and typing compared to normal controls are reflected in the brainwave signal patterns [ABSTRACT FROM AUTHOR]

Recently, deep learning has proven to be a successful technique especially in medical image analysis. This paper aims to highlight the importance of deep learning architectures in lung disease diagnosis using CXR images. Related articles were identified through searches of electronic resources, including IEEE, Springer, Elsevier, PubMed, Nature and, Hindawi digital library. The inclusion of articles was based on high-performance artificial intelligence models, developed for the classification of possible findings in CXR images published from 2018 to 2023. After the quality assessment of papers, 129 articles were included according to PRISMA guidelines. Papers were studied by types of lung disease, data source, algorithm type, and outcome metrics. Three main categories of computer-aided lung disease detection were covered: traditional machine learning, deep learning-based methods, and combination of aforementioned methods for all lung diseases. The results showed that various pre-trained networks including ResNet, VGG, and DenseNet, are the most frequently used CNN architectures and would result in a notable increase in sensitivity and accuracy. Recent research suggests that utilizing a combination of deep networks with a robust machine learning classifier can outperform deep learning approaches that rely solely on fully connected neural networks as their classifier. Finally, the limitations of the existing literature and potential future research opportunities in possible findings in CXR images using deep learning architectures are discussed in this systematic review. • A systematic review based on PRISMA framework on machine learning applications to analyze chest X-ray images. • Trends of architectures used in last 5 years. • Preprocessing and processing techniques used by the researchers. • Future trends and open problems. [ABSTRACT FROM AUTHOR]

Medical diagnosis based on machine learning has received growing interest in recent years. However, traditional classification algorithms often fail to appropriately deal with medical datasets because of their high dimensionality. Manifold learning is a branch of nonlinear dimension reduction algorithms that can map the high dimensional data into a low-dimensional space. In this paper, we propose a novel manifold-based medical diagnosis algorithm named Discriminative Locally Linear Mapping (DL2M). DL2M is built on the basis of the well-known manifold leaning algorithm LLE (Locally Linear Embedding). It incorporates the discriminative information of training data into the manifold transformation of LLE, and then propagates the discriminative mapping into out-of-sample extension. DL2M is not only advantageous in preserving the local structure of original manifold, but also maps the different classes of data as far as possible in the low-dimensional feature space. The time complexity of DL2M algorithm is also discussed. Sufficient experimental results demonstrate that our method exhibits promising classification performance on the real-world medical datasets. [ABSTRACT FROM AUTHOR]

A hybrid computational method based on the extreme learning machine (ELM) neural network for classification and the evolutionary genetic algorithms (GA) for feature selection is presented in this paper. The dimension of the feature space is reduced by the GA in this scheme and only the appointed features are selected. The data is then passed to an ELM neural network for the classification phase. An automated system for the diagnosis of Parkinson's Disease (PD) based on gait data set is proposed by using the GA-ELM method. PD is a neurodegenerative disease that may cause change in the central nervous causing disturbance to the gait cycle duration. Our hybrid GA-ELM algorithm has produced an optimized diagnosis of PD from healthy subjects given in the gait dataset with accuracy 93.5%, and with five effective features that reduce the original dataset dimension. [ABSTRACT FROM AUTHOR]

Confident prediction is highly relevant in machine learning; for example, in applications such as medical diagnoses, wrong prediction can be fatal. For classification, there already exist procedures that allow to not classify data when the confidence in their prediction is weak. This approach is known as classification with reject option. In this paper, we provide a new methodology for this approach. Predicting a new feature via a confidence set, we ensure an exact control of the probability of classification. Moreover, we show that this methodology can be implemented easily, in a semi-supervised way, and has attractive theoretical and numerical properties. [ABSTRACT FROM AUTHOR]

Leukocytes, also known as white blood cells, are a group of cells that protect the body against infections, which is an important part of the immune system. The classification of white blood cells is widely used to diagnose various diseases, such as AIDS, leukemia, myeloma and anemia. However, the conventional methods to classify white blood cells are time consuming and prone to errors. In this paper, one of the most popular neural networks, convolutional neural network (CNN) is selected to differentiate between different types of white blood cells, namely, eosinophil, lymphocyte, monocyte and neutrophil. The CNN was coupled with Alexnet, Resnet50, Densenet201 and GoogleNet in turn, and trained with the Kaggle Dataset. Then, Gaussian and median filters were applied separately to the images in the database. The new images were classified again by the CNN with each of the four networks. The results obtained after applying the two filters to the images were better than the results obtained with the original data. The research results make it easier to diagnose blood related diseases. [ABSTRACT FROM AUTHOR]

Hematology is the study of diagnosis and treatment options for blood diseases, including cancer. Cancer is considered one of the deadliest diseases across all age categories. Diagnosing such a deadly disease at the initial stage is essential to cure the disease. Hematologists and pathologists rely on microscopic evaluation of blood or bone marrow smear images to diagnose blood-related ailments. The abundance of overlapping cells, cells of varying densities among platelets, non-illumination levels, and the amount of red and white blood cells make it more difficult to diagnose illness using blood cell images. Pathologists are required to put more effort into the traditional, time-consuming system. Nowadays, it becomes possible with machine learning and deep learning techniques, to automate the diagnostic processes, categorize microscopic blood cells, and improve the accuracy of the procedure and its speed as the models developed using these methods may guide an assisting tool. In this article, we have acquired, analyzed, scrutinized, and finally selected around 57 research papers from various machine learning and deep learning methodologies that have been employed in the diagnosis of leukemia and its classification over the past 20 years, which have been published between the years 2003 and 2023 by PubMed, IEEE, Science Direct, Google Scholar and other pertinent sources. Our primary emphasis is on evaluating the advantages and limitations of analogous research endeavors to provide a concise and valuable research directive that can be of significant utility to fellow researchers in the field. • Use of machine learning techniques for the diagnostic purpose • Diagnosis and classification of leukemia using computer aided methods • Systematic review of the work done so far in the field of leukemia research • Machine learning and deep learning are capable to proceed large amount of data for higher accuracy. [ABSTRACT FROM AUTHOR]

The heart is a vital organ in the human body. In the process of diagnosing heart diseases, several data are often generated and various data mining procedures are often utilized in sieving out the most useful data for ascertaining the presence or absence of the disease, as a final decision. The set of useful data which is eventually utilised in making this final decision often contains hidden patterns. The excavation and analysis of these hidden patterns is often carried out, using data classification techniques and comparative analysis between different data classification techniques in order to determine the most efficient one for this purpose, constitute an aspect of data mining where there exists unabated research efforts. In this paper, a heart disease diagnosis decision support system has been developed. The system makes use of 13 attributes and four machine learning algorithms (Xgboost, Catboost, LGBM and KNN) in carrying out the data classification process for determining the presence of heart diseases. A performance analysis process carried out amongst these four data classification techniques in detecting heat diseases, revealed that Xgboost has the best performance, as it gives a higher rate of true positives and lower rate of false negatives, as well as a higher level of accuracy, compared to the other three classification techniques considered. [ABSTRACT FROM AUTHOR]

In this paper, a novel Hybrid Deep Ensemble (HDE) is proposed for automatic speech disfluency classification on a sparse speech dataset. Categorizations of speech disfluencies for diagnosis of speech disorders have so long relied on sophisticated deep learning models. Such a task can be accomplished by a straightforward approach with high accuracy by the proposed model which is an optimal combination of diverse machine learning and deep learning algorithms in a hierarchical arrangement which includes a deep autoencoder that yields the compressed latent features. The proposed model has shown considerable improvement in downgrading processing time overcoming the issues of cumbersome hyper-parameter tuning and huge data demand of the deep learning algorithms with high classification accuracy. Experimental results show that the proposed Hybrid Deep Ensemble has superior performance compared to the individual base learners, and the deep neural network as well. The proposed model and the baseline models were evaluated in terms of Cohen's kappa coefficient, Hamming loss, Jaccard score, F-score and classification accuracy. [ABSTRACT FROM AUTHOR]

Medical diagnosis is the process of determining which disease or medical condition explains a person's determinable signs and symptoms. Diagnosis of most diseases is very expensive as many tests are required for predictions. This paper aims to introduce an improved hybrid approach for training the adaptive network based fuzzy inference system (ANFIS). It incorporates hybrid learning algorithms least square estimates with Levenberg-Marquardt algorithm using analytic derivation for computation of Jacobian matrix, as well as code optimisation technique, which indexes membership functions. The goal is to investigate how certain diseases are affected by patient's characteristics and measurement such as abnormalities or a decision about the presence or absence of a disease. In order to achieve an accurate diagnosis at this complex stage of symptom analysis, the physician may need efficient diagnosis system to classify and predict patient condition by using an adaptive neuro fuzzy inference system (ANFIS) pre-processed by grid partitioning. The proposed hybridised intelligent technique was tested with Statlog heart disease and Hepatitis disease datasets obtained from the University of California at Irvine's (UCI) machine learning repository. The robustness of the performance measuring total accuracy, sensitivity and specificity was examined. In comparison, the proposed method was found to achieve superior performance when compared to some other related existing methods. [ABSTRACT FROM AUTHOR]

The accuracy-transparency trade-off is one of the most notable challenges when applying machine learning tools in the medical domain. Nefclass is a popular neuro-fuzzy classifier in medical diagnosis systems. Nefclass performs increasingly poorly as the data skewness increases. This paper presents a combined approach to improve the classification accuracy and interpretability of the Nefclass classifier, when feature values of the training and testing datasets exhibit positive skewness. The proposed model consists of two steps. Firstly, a modified Nefclass classifier embedded with a choice of two alternative discretization methods, MME and CAIM is implemented. Secondly, we devised a new rule pruning method based on the Habermans' adjusted residual to reduce the size of the resulting ruleset. This rule-pruning method improves the interpretability of Nefclass without significant accuracy deterioration. Moreover, a hybrid approach combining the two approaches provides a considerable improvement in classification accuracy and transparency of Nefclass on skewed data. [ABSTRACT FROM AUTHOR]

Cervical cancer is regarded as a serious threats to humanity, globally and this is a vital disease with huge spreading of virus that affects the health of humans. The virus is spreading at a rapid rate through mosquitoes that even may kill the one who is affected with cervical cancer. In this paper, we develop a quick response system that certainly finds the disease through a faster validation process. The study uses Convolutional Neural Network (CNN) as a deep learning model that classifies and predicts the condition or the infection status of a patient. The study uses a pre-processing model and a feature extraction model to prepare the image datasets for classification. The simulation is conducted to validate the effectiveness of the model over cervical cancer image datasets i.e. the blood samples of humans. The validation shows that the proposed method effectively classifies the patients in a faster manner than the other deep learning models. [ABSTRACT FROM AUTHOR]

Parkinson’s disease is a progressive neurological condition that affects motor abilities. Common symptoms include tremors, muscle stiffness, and difficulty with coordinated movements. A variety of efforts are underway to address these issues and improve diagnostic precision in Parkinson’s disease. This paper employs well-known machine-learning techniques to improve diagnostic accuracy. A variety of individual and ensemble AI models have been proposed, including Random Forest, Decision Tree, Logistic Regression, Gradient Boosting, Support Vector Machine, Stacking, and Bagging Ensemble classifiers. Three scenarios are applied to two standard benchmark datasets. The best performance is achieved when the Stacking Ensemble classifier is utilized, where the Support Vector Machine and Gradient Boosting are engaged for extracting features and Logistic Regression for classifying Parkinson’s disease. The Stacking Ensemble classifier reaches 94.87% accuracy and 90.00% AUC for the first dataset, while for the second dataset, 96.18% accuracy and 96.27% AUC are recorded. The final results demonstrate the importance of the suggested framework, which can help to improve the overall diagnosis outcomes.

Dengue is regarded as a serious threats to humanity, globally and this is a vital disease with huge spreading of virus that affects the health of humans. The virus is spreading at a rapid rate through mosquitoes that even may kill the one who is affected with dengue. In this paper, we develop a quick response system that certainly finds the disease through a faster validation process. The study uses artificial neural network (ANN) as a deep learning model that classifies and predicts the condition or the infection status of a patient. The study uses a preprocessing model and a feature extraction model to prepare the image datasets for classification. The simulation is conducted to validate the effectiveness of the model over dengue image datasets i.e. the blood samples of humans. The validation shows that the proposed method effectively classifies the patients in a faster manner than the other deep learning models. [ABSTRACT FROM AUTHOR]

Medical diagnosis is a complicated task and plays a vital role in saving human lives so it needs to be executed accurately and efficiently. An appropriate and accurate computer based automated decision support system is required to reduce cost for achieving clinical tests. This paper provides an insight into machine learning techniques used in diagnosing various diseases. Various data mining classifiers have been discussed which has emerged in recent years for efficient and effective disease diagnosis. [ABSTRACT FROM AUTHOR]

Application of machine learning to medical diagnosis entails facing two major issues, namely, a necessity of learning comprehensible models and a need of coping with imbalanced data phenomenon. The first one corresponds to a problem of implementing interpretable models, e.g., classification rules or decision trees. The second issue represents a situation in which the number of examples from one class (e.g., healthy patients) is significantly higher than the number of examples from the other class (e.g., ill patients). Learning algorithms which are prone to the imbalance data return biased models towards the majority class. In this paper, we propose a probabilistic combination of soft rules, which can be seen as a probabilistic version of the classification rules, by introducing new latent random variable called conjunctive feature. The conjunctive features represent conjunctions of values of attribute variables (features) and we assume that for given conjunctive feature the object and its label (class) become independent random variables. In order to deal with the between class imbalance problem, we present a new estimator which incorporates the knowledge about data imbalanceness into hyperparameters of initial probability of objects with fixed class labels. Additionally, we propose a method for aggregating sufficient statistics needed to estimate probabilities in a graph-based structure to speed up computations. At the end, we carry out two experiments: (1) using benchmark datasets, (2) using medical datasets. The results are discussed and the conclusions are drawn. [ABSTRACT FROM AUTHOR]

Modern medical diagnosis heavily rely on bio-medical and clinical data. Machine learning algorithms have proven effectiveness in mining this data to provide an aid to the physicians in supporting their decisions. In response, machine learning based approaches were developed to address this problem. These approaches vary in terms of effectiveness and computational cost. Attention has been paid towards non-communicable diseases as they are very common and have life threatening risk factors. The diagnosis of diabetes or breast cancer can be considered a binary classification problem. This paper proposes a new machine learning based algorithm, Geometrical Driven Diagnosis (GDD), to diagnose diabetes and breast cancer with accuracy up to 99.96% and 95.8% respectively. [ABSTRACT FROM AUTHOR]

The expanding area of Artificial Intelligence is playing a vital role in healthcare practices and research, and as medical field is rich in data can become difficult to interpret, the AI techniques present the preeminent solution to enhance the medical field achievements, thus as novel epidemiology and pathogens presents a critical and emerging issue for global health, the aim of the work presented in this paper is to structure a CBR framework that aid in the patients diagnosis of novel epidemiology presence, the novel pandemic Corona-virus disease (COVID19). The objective of this study is to highlight the Case Based Reasoning (CBR) AI method which is one of the most successful applied methods in the medical field, used for analysis, prediction, diagnosis, and recommendation treatment. This study proposes a CBR conceptual framework for COVID-19 disease prediction, able to aid in the diagnosis, to provide self-health assistant and to guide people in self testing and checking. [ABSTRACT FROM AUTHOR]

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[Display omitted] • Although there is no definitive cure for Alzheimer's disease, early diagnosis of the disease can slow the process and improve quality of life. • It has been reported that 10–15% of cases fail to be diagnosed even by experienced specialists. • Biomedical imaging (such as CT, fMRI, PET) methods require high cost and time. • The reason for preference in this study is the lower cost and time requirement of EEG signals. • It is found that the system is robust and reliable as it reached high average accuracies: • Traditional Classifiers: 98.28% using nineteen EEG electrodes and with an accuracy of 96.55% dealing with only three central electrodes. Cognitive and behavioral deficits are some of the symptoms of Alzheimer's disease, a neurological disease caused by brain deterioration. Early diagnosis of the disease minimizes the disease's progression. In this way, patients' living standards can be maintained. An experienced specialist will assess the results of grueling diagnostic tests and a costly diagnosis process. This motivation led to the creation of a fully automated computer system, which uses the proposed methods to detect Alzheimer's disease from EEG signals acquired from a minimum number of electrodes: three central lobe electrodes. This work presents the advantages of implementing the proposed system. Two different datasets were presented to evaluate the system's performance. After preprocessing the raw EEG data, wavelet transforms were applied to calculate statistical properties. First, the paper discusses the performance of different traditional machine learning classifiers: Diagonal Discriminant Analysis, Support Vector Machine (LSVM, RBF), K-Nearest Neighbors, Random Forest, Logistic Regression, and Naïve Bayes. By comparing the classifiers' performance, it is found that Naïve Bayes and LSVM classifiers yield the highest performance of 96.55% and 95.69% when applied to the first dataset and 96.55% and 94.52% for the second dataset, respectively. "ResNet-50" Convolutional Neural Network classifier was implemented to demonstrate the capability of constructing an effective AD diagnosing system. It yields an accuracy of 97.8261% when applied to the first dataset. This work shows how Deep Learning improves the performance of classification in early medical diagnosis, which will enhance different diseases' treatment, as "Resnet – 50" convolutional neural network classifier outperformed Naïve Bayes Classifier. [ABSTRACT FROM AUTHOR]

Chagas disease, caused by the Trypanosoma cruzi (T. cruzi) parasite, is the third most common parasitosis worldwide. Most of the infected subjects can remain asymptomatic without an opportune and early detection or an objective diagnostic is not conducted. Frequently, the disease manifests itself after a long time, accompanied by severe heart disease or by sudden death. Thus, the diagnosis is a complex and challenging process where several factors must be considered. In this paper, a novel pipeline is presented integrating temporal data from four modalities (electrocardiography signals, echocardiography images, Doppler spectrum, and ELISA antibody titers), multiple features selection analyses by a univariate analysis and a machine learning-based selection. The method includes an automatic dichotomous classification of animal status (control vs. infected) based on Random Forest, Extremely Randomized Trees, Decision Trees, and Support Vector Machine. The most relevant multimodal attributes found were ELISA (IgGT, IgG1, IgG2a), electrocardiography (SR mean, QT and ST intervals), ascending aorta Doppler signals, and echocardiography (left ventricle diameter during diastole). Concerning automatic classification from selected features, the best accuracy of control vs. acute infection groups was 93.3 ± 13.3% for cross-validation and 100% in the final test; for control vs. chronic infection groups, it was 100% and 100%, respectively. We conclude that the proposed machine learning-based approach can be of help to obtain a robust and objective diagnosis in early T. cruzi infection stages. [ABSTRACT FROM AUTHOR]