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Due to the vast amount of patient health data, automated healthcare systems still struggle to classify and diagnose various ailments. Learning redundant data also reduces categorization accuracy. A Deep Belief Network (DBN) has been used to precisely extract the most important aspects from clinical data by ignoring irrelevant/redundant features. Due of many learning variables, training is complicated. Similarly, the hybrid model has been employed by ensemble Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) to categorize diseases. But, its efficiency depends on the proper choice of kernels and hyper-parameters. Therefore, this paper develops an efficient feature extraction and classification model for healthcare systems. First, several medical data related to the patient's health are collected. Then, an Optimized DBN (ODBN) model is presented for maximizing the accurateness of DBN by optimizing the learning variables depends on the Ant Lion Optimization (ALO) algorithm. With learning ODBN, the most relevant features are extracted with reduced computational complexity. After that, the CNN-LSTM with Unsupervised Fine-tuned Deep Self-Organizing Map (UFDSOM)-based classifier model is designed to categorize the extracted features into categories of illnesses. In this novel classifier, dropout normalization and parameter tuning processes are applied to avoid overfitting and optimize the hyper-parameters, which results in a less training period. In the end, studies utilizing publically accessible datasets show that the ODBN with CNN-LSTM-UFDSOM system outperforms classical models by 98.23%. [ABSTRACT FROM AUTHOR]

The Emergency Departments (EDs) in health centres located in the main areas of Saudi Arabia have heavy patient inflow because of the pandemic, viral infections, and even on some special occasions like Umrah or Hajj, where pilgrims who travel from one place to another with serious disorders. Other than the EDs, it was important to observe the patient’s activities from ED to other wards in the region or the hospital to track the spread of viral diseases. In this case, deep learning (DL) and machine learning (ML) methods have been used to track the target audience and classify the data into many classes. With this motivation, this study develops an artificial rabbit optimization with a machine learning-based healthcare data classification (AROML-HDC) technique for EDs. The AROML-HDC technique monitors and tracks the patient visit data, treatment given, and length of stay (LOS). In addition, the AROML-HDC technique designs an effective ARO algorithm for the optimal selection of feature subsets. Next, the class-specific cost regulation extreme learning machine (CSCR-ELM) classifier is applied for effective medical data classification. Finally, the grasshopper optimization algorithm (GOA) was used to adjust the parameters related to the CSCR-ELM classifier. The experimental outcome of the AROML-HDC approach is tested on the benchmark Cleveland dataset and the Statlog dataset comprising 297 and 270 samples, respectively. The simulation results signify the improved performance of the AROML-HDC technique over other recent methods with maximum accuracy of 93.22% and 94.05% Cleveland dataset and the Statlog dataset, respectively.

Recently, big data analytics have gained significant attention in healthcare industry due to generation of massive quantities of data in various forms such as electronic health records, sensors, medical imaging, and pharmaceutical details. However, the data gathered from various sources are intrinsically uncertain owing to noise, incompleteness, and inconsistency. The analysis of such huge data necessitates advanced analytical techniques using machine learning and computational intelligence for effective decision making. To handle data uncertainty in healthcare sector, this article presents a novel metaheuristic rough set-based feature selection with rule-based medical data classification (MRSFS-RMDC) technique on MapReduce framework. The proposed MRSFS-RMDC technique designs a butterfly optimization algorithm for minimal rough set selection. In addition, Hadoop MapReduce is applied to process massive quantity of data. Moreover, a rule-based classification approach named Repeated Incremental Pruning for Error Reduction (RIPPER) is used with the inclusion of a set of conditional rules. The RIPPER will scale in a linear way with the number of training records utilized and is suitable to build models with data uncertainty. The proposed MRSFS-RMDC technique is validated using benchmark dataset and the results are inspected under varying aspects. The experimental results highlighted the supremacy of the MRSFS-RMDC technique over the recent state of art methods in terms of different performance measures. The proposed methodology has achieved a higher F-score of 96.49%.

With the exponential developments of wireless networking and inexpensive Internet of Things (IoT), a wide range of applications has been designed to attain enhanced services. Due to the limited energy capacity of IoT devices, energy-aware clustering techniques can be highly preferable. At the same time, artificial intelligence (AI) techniques can be applied to perform appropriate disease diagnostic processes. With this motivation, this study designs a novel squirrel search algorithm-based energy-aware clustering with a medical data classification (SSAC-MDC) model in an IoT environment. The goal of the SSAC-MDC technique is to attain maximum energy efficiency and disease diagnosis in the IoT environment. The proposed SSAC-MDC technique involves the design of the squirrel search algorithm-based clustering (SSAC) technique to choose the proper set of cluster heads (CHs) and construct clusters. Besides, the medical data classification process involves three different subprocesses namely pre-processing, autoencoder (AE) based classification, and improved beetle antenna search (IBAS) based parameter tuning. The design of the SSAC technique and IBAS based parameter optimization processes show the novelty of the work. For showcasing the improved performance of the SSAC-MDC technique, a series of experiments were performed and the comparative results highlighted the supremacy of the SSAC-MDC technique over the recent methods. [ABSTRACT FROM AUTHOR]

Recently, artificial intelligence (AI) including machine learning (ML) and deep learning (DL) models has been commonly employed for the automated disease diagnosis process. AI in biological and biomedical imaging is an emerging area and will be a future trend in the field. At the same time, biomedical images can be used for the classification of Rheumatoid arthritis (RA) diseases. RA is an autoimmune illness that affects the musculoskeletal system causing systemic, inflammatory and chronic effects. The disease frequently becomes progressive and decreases physical function, causing articular damage, suffering, and fatigue. After a time, RA causes harm to the cartilage of the joints and bones, weakens the tendons and joints, and finally causes joint destruction. Sensors (thermal infrared camera sensor, accelerometers and wearable sensors) are more commonly employed to collect data for RA. This study develops an Automated Rheumatoid Arthritis Classification using an Arithmetic Optimization Algorithm with Deep Learning (ARAC-AOADL) model. The goal of the presented ARAC-AOADL technique lies in the classification of health disorders depending upon RA and orthopaedics. Primarily, the presented ARAC-AOADL technique pre-processes the input images by median filtering (MF) technique. Then, the ARAC-AOADL technique uses AOA with an enhanced capsule network (ECN) model to produce feature vectors. For RA classification, the ARAC-AOADL technique uses a multi-kernel extreme learning machine (MKELM) model. The experimental result analysis of the ARAC-AOADL technique on a benchmark dataset reported a maximum accuracy of 98.57%. Therefore, the ARAC-AOADL technique can be employed for accurate and timely RA classification. [ABSTRACT FROM AUTHOR]

At present times, computer aided diagnosis (CAD) models become familiar in the healthcare sector. Medical diagnosis is identified to be subjective and it is based on the available data as well as physician's experience. Machine learning (ML) techniques are commonly employed to design effective CAD models due to its stronger ability of identifying the complicated relationship in the medical data. This paper aims to develop a novel data classification model using squirrel search algorithm (SSA) with Mode Ranking method called MRISSA for healthcare diagnosis. Experiments were done on heart disease datasets available in Kaggle to evaluate the performance of the suggested technique. The outcomes show how effective the hybrid (MRISSA+ XGBoost) strategy is improved. [ABSTRACT FROM AUTHOR]

Recently, medical data classification becomes a hot research topic among healthcare professionals and research communities, which assist in the disease diagnosis and decision making process. The latest developments of artificial intelligence (AI) approaches paves a way for the design of effective medical data classification models. At the same time, the existence of numerous features in the medical dataset poses a curse of dimensionality problem. For resolving the issues, this article introduces a novel feature subset selection with artificial intelligence based classification model for biomedical data (FSS-AICBD) technique. The FSS-AICBD technique intends to derive a useful set of features and thereby improve the classifier results. Primarily, the FSS-AICBD technique undergoes min-max normalization technique to prevent data complexity. In addition, the information gain (IG) approach is applied for the optimal selection of feature subsets. Also, group search optimizer (GSO) with deep belief network (DBN) model is utilized for biomedical data classification where the hyperparameters of the DBN model can be optimally tuned by the GSO algorithm. The choice of IG and GSO approaches results in promising medical data classification results. The experimental result analysis of the FSS-AICBD technique takes place using different benchmark healthcare datasets. The simulation results reported the enhanced outcomes of the FSS-AICBD technique interms of several measures. [ABSTRACT FROM AUTHOR]

Colorectal cancer is one of the most commonly diagnosed cancers and it develops in the colon region of large intestine. The histopathologist generally investigates the colon biopsy at the time of colonoscopy or surgery. Early detection of colorectal cancer is helpful to maintain the concept of accumulating cancer cells. In medical practices, histopathological investigation of tissue specimens generally takes place in a conventional way, whereas automated tools that use Artificial Intelligence (AI) techniques can produce effective results in disease detection performance. In this background, the current study presents an Automated AI-empowered Colorectal Cancer Detection and Classification (AAI-CCDC) technique. The proposed AAICCDC technique focuses on the examination of histopathological images to diagnose colorectal cancer. Initially, AAI-CCDC technique performs preprocessing in three levels such as gray scale transformation, Median Filtering (MF)-based noise removal, and contrast improvement. In addition, Nadam optimizer with EfficientNet model is also utilized to produce meaningful feature vectors. Furthermore, Glowworm Swarm Optimization (GSO) with Stacked Gated Recurrent Unit (SGRU) model is used for the detection and classification of colorectal cancer. The proposed AAI-CCDC technique was experimentally validated using benchmark dataset and the experimental results established the supremacy of the proposed AAI-CCDC technique over conventional approaches. [ABSTRACT FROM AUTHOR]

This study presents a novel method to detect the medical application based on Quantum Computing (QC) and a few Machine Learning (ML) systems. QC has a primary advantage i.e., it uses the impact of quantum parallelism to provide the consequences of prime factorization issue in a matter of seconds. So, this model is suggested for medical application only by recent researchers. A novel strategy i.e., Quantum Kernel Method (QKM) is proposed in this paper for data prediction. In this QKM process, Linear Tunicate Swarm Algorithm (LTSA), the optimization technique is used to calculate the loss function initially and is aimed at medical data. The output of optimization is either 0 or 1 i.e., odd or even in QC. From this output value, the data is identified according to the class. Meanwhile, the method also reduces time, saves cost and improves the efficiency by feature selection process i.e., Filter method. After the features are extracted, QKM is deployed as a classification model, while the loss function is minimized by LTSA. The motivation of the minimal objective is to remain faster. However, some computations can be performed more efficiently by the proposed model. In testing, the test data was evaluated by minimal loss function. The outcomes were assessed in terms of accuracy, computational time, and so on. For this, databases like Lymphography, Dermatology, and Arrhythmia were used. [ABSTRACT FROM AUTHOR]

In recent years, technology in medicine has shown a significant advance due to artificial intelligence becoming a framework to make accurate medical diagnoses. Models like Multilayer Perceptrons (MLPs) can detect implicit patterns in data, allowing identifying patients conditions that cannot be seen easily. MLPs consist of biased neurons arranged in layers, connected by weighted connections. Their effectiveness depends on finding the optimal weights and biases that reduce the classification error, which is usually done by using the Back Propagation algorithm (BP). But BP has several disadvantages that could provoke the MLP not to learn. Metaheuristics are alternatives to BP that reach high-quality solutions without using many computational resources. In this work, the Cellular Genetic Algorithm (CGA) with a specially designed crossover operator called Damped Crossover (DX), is proposed to optimise weights and biases of the MLP to classify medical data. When compared against state-of-the-art algorithms, the CGA configured with DX obtained the minimal Mean Square Error value in three out of the five considered medical datasets and was the quickest algorithm with four datasets, showing a better balance between time consumed and optimisation performance. Additionally, it is competitive in enhancing classification quality, reaching the best accuracy with two datasets and the second-best accuracy with two of the remaining.

Nowadays, medical data classification plays an important role in healthcare informatics applications such as disease prediction, classification, etc. The recently developed machine learning and deep learning models are commonly employed for effective medical data classification, which can be applied for disease diagnosis. The existing techniques make few shortcomings in terms of computational complexity, higher-dimensional features, higher execution time, etc. To tackle these issues, this paper develops a new classification model with metaheuristic algorithm based optimal feature selection for Chronic Kidney Disease diagnosis. Initially, the data with missing values were evacuated in the pre-processing stage. Then, the best subset of features selected by a metaheuristic algorithm named Oppositional based FireFly Optimization algorithm, which helps in the prediction or classification of the disease more accurately. The incorporation of oppositional based learning concept helps to improve the convergence rate of FireFly algorithm. For classification, Deep Neural Network was proposed to diagnose the existence of CKD. The effectiveness of the proposed feature selection-based classifier was tested on the dataset as measures of sensitivity, specificity, and accuracy. The results concluded that the proposed algorithm achieved a high accuracy rate when compared to the algorithms of existing classifier models. [ABSTRACT FROM AUTHOR]

This paper presents an approach that combines the data preprocessing, feature weighting, and the improved antlion optimization algorithm for effective and efficient classification of gastrointestinal lesions. A high-dimensional gastrointestinal lesion dataset that consists of extracted texture, color, and shape features from the colonoscopy videos is obtained from the UCI repository. The data has certain imperfections such as the presence of zero-valued features, outliers, and dominant features. So, it is preprocessed to cope with these problems. Then, feature weighting is used to boost the classification performance by assigning the weights to the features according to their relevance in classification. The improved antlion optimization algorithm is used to search for feature weights and the parameters of the Support Vector Machines simultaneously. The experiments are performed using different combinations of features and endoscopic images to analyse the performance. The outcomes show that the combination of texture and color features from NBI images is the best. The accuracy of 97.37% and 98.68% for multi-class and binary classification problems respectively is attained using only ∼ 31% features. Moreover, feature reduction helps to lower the runtime of the classifier by approx. 60%. In conclusion, a better approach is presented for colorectal lesions classification that competes with well-experienced colonoscopists and outperforms the existing methods. [ABSTRACT FROM AUTHOR]

In a computational medical model, diagnosis is the classification of disease status in the terms of abnormal or positive, normal or negative or intermediate stages. Different Machine learning techniques such as artificial neural networks (ANNs) are extensively and successfully used in disease diagnosis. However, there is no single classifier that can solve all classification problems. Selecting an optimal classifier for a problem is difficult, and it has become a relevant subject in the area. This paper focuses on grammar-based automatic programming (GAP) to build optimized discriminant functions for medical data classification in any arbitrary language. These techniques have an implicit power of automatic feature selection and feature extraction. This work carries out an in-depth investigation of the use of different GAP algorithms in the medical data classification problem. The objective is to identify the benefits and limitations of algorithms of this nature in the current problem. Classical classifiers were also considered for comparison purposes. Fourteen medical data sets were used in the experiments, and seven performance measures such as accuracy, sensitivity, specificity, precision, geometric-mean, F-measure, and false-positive rate are used to evaluate the performance of the produced classifier. The multiple criteria decision analysis (MCDA) demonstrates that GAP approaches are able to produce suitable classifiers for a given problem, and the GS performs better than other classical classifiers in medical data classification. [ABSTRACT FROM AUTHOR]

This study presents a novel algorithm which combines active learning (AL) and transfer learning for medical data classification. The main idea of the proposed algorithm is iteratively querying a small number of informative unlabelled target samples, and, at the same time, removing the source samples which do not fit with the posterior probability distributions in the target domain, so as to combine the basic idea of AL with transfer learning. The experimental results obtained in the classification of the datasets from the University of California Irvine (UCI) Machine Learning Repository and The Cancer Imaging Archive (TCIA) confirm the effectiveness of the proposed algorithm.

Medical Data is commonly seen as heterogeneous, unbalanced, high-dimensional, noiserelated and anomaly-related. It covers scientific knowledge and genetic data, as well as the principle of biomedical computation. Data observations across the world have been spread in the past several years. The effect of this development is felt everywhere from business, science, medical data and technologies. A significant number of deaths each year in India are caused by errors in the health care system, and many thousands experience ill-effects for similar reasons. Electronic Health Records (EHR) collection is one of the most significant advances as it facilitates the improvement of new technologies for error prevention, cost reduction and health advancement. The proposed research addresses the usage of EHR in the study of related data using Machine Learning (ML) techniques. The usage of machine intelligence techniques enhances efficiency and reduces the error rate which strengthens health treatment for patients. The EHR used in emergency clinics contains a variety of data, as shown by the doctor's arguments for accurate recognition. Information and data can be shared on the basis of these special needs. Such studies are used by doctors to examine the patient's history of clinical records and to track patient treatment. Each time a patient enters the emergency department, the doctor makes another case report and, during the diagnostic procedure, tries to explore the relationship between the patient and the family-related person in order to characterize the diagnosis and health status of the patient. The proposed work uses a Weight Based Labeled Classifier using a Machine Learning (WbLCML) model designed to improve diagnostic efficiency, accuracy and reliability. The proposed model is compared to traditional methods and the results suggest that the proposed model is better suited to the proper classification of medical data. [ABSTRACT FROM AUTHOR]

This paper proposes an integrated system neutrosophic C-means-based attribute weighting-kernel extreme learning machine (NCMAW-KELM) for medical data classification using NCM clustering and KELM. To do that, NCMAW is developed, and then combined with classification method in classification of medical data. The proposed approach contains two steps. In the first step, input attributes are weighted using NCMAW method. The purpose of the weighting method is twofold: (i) to improve the classification performance in the classification of the medical data, (ii) to transform from nonlinearly separable dataset to linearly separable dataset. Finally, KELM algorithm is used for medical data classification purpose. In KELM algorithm, four types of kernels, such as Polynomial, Sigmoid, Radial basis function and Linear, are used. The simulation result on our three datasets demonstrates that the sigmoid kernel is outperformed to ELM in most cases. From the results, NCMAW-KELM approach may be a promising method in medical data classification problem. [ABSTRACT FROM AUTHOR]

Medical data classification is the process of transforming descriptions of medical diagnoses and procedures into universal medical code numbers. The diagnoses and procedures are usually taken from a variety of sources within the healthcare record, such as the transcription of the physician's notes, laboratory results, radiologic results and other sources. However, there exist many frequency distribution problems in these domains. Hence, this paper intends to develop an advanced and precise medical data classification approach for diabetes and breast cancer dataset. With the knowledge of the features and challenges persisting with the state-of-the-art classification methods, deep learning-based medical data classification methodology is proposed here. It is well known that deep learning networks learn directly from the data. In this paper, the medical data is dimensionally reduced using Principle Component Analysis (PCA). The dimensionally reduced data are transformed by multiplying by a weighting factor, which is optimized using Whale Optimization Algorithm (WOA), to obtain the maximum distance between the features. As a result, the data are transformed into a label-distinguishable plane under which the Deep Belief Network (DBN) is adopted to perform the deep learning process, and the data classification is performed. Further, the proposed WOA-based DBN (WOADBN) method is compared with the Neural Network (NN), DBN, Generic Algorithm-based NN (GANN), GADBN, Particle Swarm Optimization (PSONN), PSO-based DBN (PSODBN), WOA-based NN (WOANN) techniques and the results are obtained, which shows the superiority of proposed algorithm over conventional methods. [ABSTRACT FROM AUTHOR]

In medical data classification, data reduction and improving classification performance are the important issues in the current scenario. In existing medical data classification methods, initially, the medical data pre-processing is performed. After pre-processing feature selection is performed, otherwise, the process is more time consuming and has poor accuracy. Here we have proposed two algorithms for enhancing the classification performance on medical data. In first proposed method Bag of Words technique is used for better feature subset selection. Subsequently, the hybrid Fuzzy-Neural Network approach used that can handle imprecision in data while classification. This combination of feature selection technique and Fuzzy-Neural Network classifier approach gives enhanced classification accuracy. In the second proposed algorithm, we have integrated data cleaning technique to improve data quality as pre-processing technique along with bag of words and Fuzzy-Neural Network, this method performs classification on clean filtered data with appropriately reduced feature set that results in more accurate classification than the existing methods. Thus in proposed approaches we have tried to handle three issues, removing noise in data, optimal feature subset selection and handling imprecision in data. The comparative study of various medical datasets in terms of accuracy shows that the two proposed algorithms perform better as compared to existing techniques and the enhancement obtained is around 3% and 17% respectively. In addition the performance of Bag of Words feature selection method used in the proposed system is compared with two feature selection methods LSFS and SFFS. [ABSTRACT FROM AUTHOR]

The immense amount of data managed during the diagnosis process overwhelms, by far, the clinicians' processing capabilities. Artificial intelligence methods like Multi-Layer Perceptrons come to help by providing a second opinion based on powerful and reliable data processing. Unfortunately, these methods often suffer from problems related to their training methods, which can lead to poor performance. Metaheuristics are promising training alternatives because of their stochastic and general-purpose nature. This work introduces a new training method based on metaheuristics, called Genetic Ant Lion Optimiser. It includes new features for dealing with the convergence problems of the original Ant Lion Optimiser and integrates a novel crossover operator for avoiding stagnation. Experiments compare our proposal against 31 state-of-the-art algorithms, over 20 different medical datasets. Classification quality metrics reflect that our approach attains a robust and efficient behaviour with the majority of the datasets, obtaining highlighted results, such as an accuracy of 1.0 with the kidney dataset (bi-class) and 0.943 with the lung-cancer dataset (multi-class). Besides, it reaches adequate convergence rates and reasonable time consumption. • A novel genetic ant lion algorithm is proposed for training multi-layer perceptrons. • A new crossover strategy is introduced for controlling elitism and avoiding stagnation. • The proposal is compared against other alternatives over twenty medical datasets. • Classification quality is improved by our proposal in most of the medical datasets. • Our approach is competitive regarding resource usage and convergence ability. [ABSTRACT FROM AUTHOR]

Developing an automatic model to precisely and quickly classify medical data is a challenging work due to lack of medical knowledge. This paper presented a transformed fuzzy neural network (TFNN) to enhance medical data classification accuracy by learning knowledge from the available medical databases. We developed a novel reward/penalty fuzzy rule base to infer the degree of transformation for each input attribute. The degree of reward/penalty was determined according to its original measured value relative to its attribute mean and standard deviation. The farther the deviation of a datum to its mean value, the larger the penalty was exerted on the datum. In contrast, a datum in the neighborhood of mean value received a positive award. Furthermore, a pattern’s desired output was considered a valuable knowledge and was treated as an extra input to the TFNN. In this study, three medical datasets are used to validate the classification accuracy. Results compared to the back propagation neural network and support vector machine verified the effectiveness of the proposed model in medical data classification. [ABSTRACT FROM AUTHOR]

In today’s medical world, the patient’s data with symptoms and diseases are expanding rapidly, so that analysis of all factors with updated knowledge about symptoms and corresponding new treatment is merely not possible by medical experts. Hence, the essential for an intelligent system to reflect the different issues and recognize an appropriate model between the different parameters is evident. In recent decades, rough set theory (RST) has been broadly applied in various fields such as medicine, business, education, engineering and multimedia. In this study, a hybrid intelligent system that combines rough set (RST) and bijective soft set theory (BISO) to build a robust classifier model is proposed. The aim of the hybrid system is to exploit the advantages of the constituent components while eliminating their limitations. The resulting approach is thus able to handle data inconsistency in datasets through rough sets, while obtaining high classification accuracy based on prediction using bijective soft sets. Toward estimating the performance of the hybrid rough-bijective soft set (RBISO)-based classification approach, six benchmark medical datasets (Wisconsin breast cancer, liver disorder, hepatitis, Pima Indian diabetes, echocardiogram data and thyroid gland) from the UCI repository of machine learning databases are utilized. Experimental results, based on evaluation in terms of sensitivity, specificity and accuracy, are compared with other well-known classification methods, and the proposed algorithm provides an effective method for medical data classification. [ABSTRACT FROM AUTHOR]

One of the most important problems in probabilistic neural network (PNN) operation is the minimization of its structure. In this paper, two heuristic approaches of PNN's pattern layer reduction are applied. The first method is based on a k-means clustering procedure. In the second approach, the candidates for the network's pattern neurons are selected on the basis of a support vector machines algorithm. Modified models are compared in the classification problems with the traditional PNN, four well-known computational intelligence algorithms (single decision tree, multilayer perceptron, support vector machines, k-means algorithm) and PNN trained by the state-of-the-art procedures. Seven medical benchmark databases are investigated and one authors' own real ovarian cancer data set. Comparison is performed on the basis of the global performance indices which depend on the accuracy, sensitivity and specificity. These indices are computed using the standard tenfold cross-validation procedure. On the basis of the reported results, we show that the algorithm based on k-means clustering is a better PNN structure reduction procedure. Furthermore, this algorithm is much less time-consuming. [ABSTRACT FROM AUTHOR]

The significance of the preprocessing stage in any data mining task is well known. Before attempting medical data classification, characteristics ofmedical datasets, including noise, incompleteness, and the existence of multiple and possibly irrelevant features, need to be addressed. In this paper, we show that selecting the right combination of preprocessing methods has a considerable impact on the classification potential of a dataset. The preprocessing operations considered include the discretization of numeric attributes, the selection of attribute subset(s), and the handling of missing values. The classification is performed by an ant colony optimization algorithm as a case study. Experimental results on 25 real-world medical datasets show that a significant relative improvement in predictive accuracy, exceeding 60% in some cases, is obtained. [ABSTRACT FROM AUTHOR]

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Data preprocessing has a profound effect on the performance of the learner. Before attempting medical data classification, characteristics of medical datasets, including noise, incompleteness, and the existence of multiple and possibly irrelevant features, need to be addressed. In this paper, we show that selecting the right combination of preprocessing methods has a considerable impact on the classification potential of a dataset. The preprocessing operations considered include the discretization of numeric attributes, the selection of attribute subset(s), and the handling of missing values. The classification is performed by an ant colony optimization algorithm as a case study. Experimental results on 25 real-world medical datasets show that a significant relative improvement in predictive accuracy, exceeding 60% in some cases, is obtained. [ABSTRACT FROM AUTHOR]

This paper introduces an automated medical data classification method using wavelet transformation (WT) and interval type-2 fuzzy logic system (IT2FLS). Wavelet coefficients, which serve as inputs to the IT2FLS, are a compact form of original data but they exhibits highly discriminative features. The integration between WT and IT2FLS aims to cope with both high-dimensional data challenge and uncertainty. IT2FLS utilizes a hybrid learning process comprising unsupervised structure learning by the fuzzy c-means (FCM) clustering and supervised parameter tuning by genetic algorithm. This learning process is computationally expensive, especially when employed with high-dimensional data. The application of WT therefore reduces computational burden and enhances performance of IT2FLS. Experiments are implemented with two frequently used medical datasets from the UCI Repository for machine learning: the Wisconsin breast cancer and Cleveland heart disease. A number of important metrics are computed to measure the performance of the classification. They consist of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. Results demonstrate a significant dominance of the wavelet–IT2FLS approach compared to other machine learning methods including probabilistic neural network, support vector machine, fuzzy ARTMAP, and adaptive neuro-fuzzy inference system. The proposed approach is thus useful as a decision support system for clinicians and practitioners in the medical practice. [ABSTRACT FROM AUTHOR]

In the field of medical informatics, the accuracy of medical data classification plays a vital role. Multi-layer Perceptron (MLP), as one of the most widely used neural networks, has been widely used in the medical fields. In recent years, the Biogeography-based Optimization (BBO) algorithm has been proposed to train MLP, but the original algorithm often encounters local minimums, slow convergence, and sensitivity to initialized values during the optimization process. To this end, this paper adopted the different probability distributions to improve the BBO (PD-BBO) algorithm to train MLP so as to improve medical data classification accuracy. These distributions include Gamma distribution, Beta distribution, Gaussian distribution, Exponential distribution, Poisson distribution, Geometric distribution, Rayleigh distribution and Weber distribution Then these different probability distributions were embed into the migration process of the BBO algorithm to replace the random distribution and the migration probability was defined. Finally, simulation experiments were carried out, and the benchmark function was used to prove the effectiveness of the proposed algorithms. And then it was used to train a multi-layer perceptron, and five medical data sets were selected for classification. After that, the performance of the standard BBO algorithm and five typical meta-heuristic algorithms were compared. The results showed that the PD-BBO algorithms to train MLP was better than the BBO algorithm and the selected meta-heuristic algorithms, and the classification accuracy has been improved to a certain extent. [Display omitted] • Adopt probability distributions (Gamma, Beta, Gaussian, Exponent, Poisson, Geometry, Rayleigh and Weber) in BBO algorithm. • Propose eight probability distributions based BBO variants. • Apply PD-BBO algorithms to train MLPNN for medical data classification problems. [ABSTRACT FROM AUTHOR]

The Artificial Neural Network (ANN) is an important machine learning tool used in medical data classification for disease diagnosis. The learning algorithm in ANN training plays a crucial role in classification performance. Various approaches have been successfully applied as a learning algorithm for ANN training. This paper performs an experimental study that investigates the performance of different metaheuristics as learning algorithms to train the ANN for medical data classification tasks. The experiments are carried out on 15 well-known medical datasets. A comparative study is conducted with the classical Levenberg–Marquardt (LM) and other thirteen recent and relevant metaheuristics. Different evaluation criteria such as accuracy, sensitivity, specificity, precision, Geometric Mean, F-Measure, false-positive rate (FPR) are considered for performance estimation. The classification results are analyzed using Multi-Criteria Decision Making (MCDM) method, and the results with analysis establish that the Equilibrium Optimizer algorithm outperforms all the other algorithms included in the comparative study. • Artificial Neural Network training using metaheuristic algorithms. • An experimental study in medical data classification. • Performance analysis using multi-criteria decision making. • Equilibrium Optimizer shows superior performance over the competitive algorithms. [ABSTRACT FROM AUTHOR]

Cardiotocography (CTG) is a simultaneous recording of fetal heart rate (FHR) and uterine contractions (UC). It is one of the most common diagnostic techniques to evaluate maternal and fetal well-being during pregnancy and before delivery. By observing the Cardiotocography trace patterns doctors can understand the state of the fetus. There are several signal processing and computer programming based techniques for interpreting a typical Cardiotocography data. Even few decades after the introduction of Cardiotocography into clinical practice, the predictive capacity of the methods remains controversial and still inaccurate. In this paper, we evaluate commonly used unsupervised clustering method Fuzzy C-mean clustering for their suitability towards clustering CTG data. We used Precision, Recall, F-Score and Rand Index as the metric to evaluate the performance. In previous work, the overall Precision, Recall and F-Score were only considered. But in this evaluation, we are going to measure class-wise Precision, Recall and F-Score to make the analysis very specific. The arrived results prove that, even though the traditional clustering methods can identify the Normal CTG patterns, they were incapable of Suspicious and Pathologic patterns. This fact was not highlighted in the previous work. [ABSTRACT FROM AUTHOR]

This paper presents a novel hybrid ensemble approach for classification in medical databases. The proposed approach is formulated to cluster extracted features from medical databases into soft clusters using unsupervised learning strategies and fuse the decisions using parallel data fusion techniques. The idea is to observe associations in the features and fuse the decisions made by learning algorithms to find the strong clusters which can make impact on overall classification accuracy. The novel techniques such as parallel neural-based strong clusters fusion and parallel neural network based data fusion are proposed that allow integration of various clustering algorithms for hybrid ensemble approach. The proposed approach has been implemented and evaluated on the benchmark databases such as Digital Database for Screening Mammograms, Wisconsin Breast Cancer, and Pima Indian Diabetics. A comparative performance analysis of the proposed approach with other existing approaches for knowledge extraction and classification is presented. The experimental results demonstrate the effectiveness of the proposed approach in terms of improved classification accuracy on benchmark medical databases. [ABSTRACT FROM AUTHOR]

Bayesian Networks encode causal relations between variables using probability and graph theory. We exploit the causal relations to detect dependency structures in database consisting of a small number of observations having high dimensions. Bypass surgery survival chance must be inferred from a database consisting of 66 medical examinations for 313 patients. Tree-like Bayesian were inferred based on mutual information and than analysed for classification of data, respect to survival. Bayesian Network approach allows us to interpret the predictions of the system thus helping the doctor in after surgery treatment prescription. In this paper we present the used methods and results on artificial data. [ABSTRACT FROM AUTHOR]

Fetching useful information from big medical datasets is a complicated task in the big data age. Various classification algorithms are used in the data mining process to analyze information from the big medical dataset. Nevertheless, these classification algorithms are insufficient to handle big medical data. This work proposes an efficient, ensemble-based classification framework for big medical data to deal with this problem. The proposed work involves initially applying the preprocessing technique to remove noise, missing values, and unwanted features from big medical data. The process selects a subset of classifiers from a pool of classifiers. The selected classifiers are combined to form a hybrid system for efficient classification. The methodology further involves incremental learning from data samples, explaining the predicted outputs, and achieving high classification performance. Java is used for simulation, and the Cleveland Heart Disease big dataset and Diabetes big dataset are used for classification. The experimental result shows that the proposed ensemble algorithm provides an efficient classification compared with existing algorithms based on accuracy, precision, F-measure, recall, and execution time.

In the last decade, machine learning (ML) techniques have been widely applied to identify different diseases. This facilitates an early diagnosis and increases the chance of survival. The majority of medical data-sets are unbalanced. Due to this, ML classification techniques give biased classification over the majority class. In this paper, a novel fitness function in Genetic Programming, for medical data classification has been proposed that handles the problem of unbalanced data. Four benchmark medical data-sets named chronic kidney disease (CKD), fertility, BUPA liver disorder, and Wisconsin diagnostic breast cancer (WDBC) have been taken from the University of California (UCI) machine learning repository. Classification is done using the proposed technique. The proposed technique achieved the best accuracy for CKD, WDBC, Fertility, and BUPA dataset as 100%, 99.12%, 85.0%, and 75.36% respectively, and the best AUC as 1.0, 0.99, 0.92, and 0.75 respectively. The result outcomes show an improvement over other GP and SVM methods that confirm the efficiency of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Classification of the privacy preserved medical data is the domain of the researchers as it stirs the importance behind hiding the sensitive data from the third-party authenticator. Ensuring the privacy of the medical records and using the disease prediction mechanisms played a remarkable role in peoples' lives such that the earlier detection of the diseases is required for earlier diagnosis. Accordingly, this article proposes a method, named Taylor gradient descent (TGD)-based actor critic neural network (ACNN), which concentrates on performing the medical data classification. Initially, the privacy of the medical data is ensured by using the key matrix developed based on the privacy utility coefficient matrix using the chronological-Whale optimization algorithm. The privacy protected data are subjected to classification by using ACNN that performs the optimal classification using the proposed TGD algorithm. The proposed TGD algorithm is the integration of Taylor series in the gradient descent algorithm that updates the optimal weight of ACNN based on the weights in the previous iterations. The analysis using the Cleveland, Switzerland, and Hungarian dataset proves that the proposed classification strategy obtains an accuracy of 0.9252, a sensitivity of 0.8419, and a specificity of 0.8387, respectively.