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An integrated review on machine learning approaches for heart disease prediction: Direction towards future research gaps

Vellameeran Fathima Aliyar, Brindha Thomas

Bio-Algorithms and Med-Systems

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2022

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Vol. 18, no. 1

69--83

EN

Objectives: To make a clear literature review on state-ofthe-art heart disease prediction models. Methods: It reviews 61 research papers and states the significant analysis. Initially, the analysis addresses the contributions of each literature works and observes the simulation environment. Here, different types of machine learning algorithms deployed in each contribution. In addition, the utilized dataset for existing heart disease prediction models was observed. Results: The performance measures computed in entire papers like prediction accuracy, prediction error, specificity, sensitivity, f-measure, etc., are learned. Further, the best performance is also checked to confirm the effectiveness of entire contributions. Conclusions: The comprehensive research challenges and the gap are portrayed based on the development of intelligent methods concerning the unresolved challenges in heart disease prediction using data mining techniques.

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Fuzzy Brain Emotional Controller for Heart Disease Diagnosis

Pham Duc Hung, Nguyen Viet-Ngu, Le Thi Minh-Tam

Annals of Computer Science and Information Systems

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2022

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Vol. 33

33--36

EN

This article provides a new way for classifying heart disease. A classifier using a controller for brain emotional learning and a fuzzy system is presented. The controller's parameter updating laws are built using the gradient descent method. The method's convergence and stability are ensured by the Lyapunov function. Using the heart disease dataset from the University of California, Irvine (UCI), the performance of the system is examined. In addition, a comparison with different classifiers is provided. The outcomes of our experiments illustrate the efficacy of our strategy.

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Ensemble classification technique for heart disease prediction with meta-heuristic-enabled training system

Kumar Parvathaneni Rajendra, Ravichandran Suban, Narayana Satyala

Bio-Algorithms and Med-Systems

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2021

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Vol. 17, no. 2

119--136

EN

Objectives: This research work exclusively aims to develop a novel heart disease prediction framework including three major phases, namely proposed feature extraction, dimensionality reduction, and proposed ensemble-based classification. Methods: As the novelty, the training of NN is carried out by a new enhanced optimization algorithm referred to as Sea Lion with CanberraDistance (S-CDF) via tuning the optimal weights. The improved S-CDF algorithm is the extended version of the existing “Sea Lion Optimization (SLnO)”. Initially, the statistical and higher-order statistical features are extracted including central tendency, degree of dispersion, and qualitative variation, respectively. However, in this scenario, the “curse of dimensionality” seems to be the greatest issue, such that there is a necessity of dimensionality reduction in the extracted features. Hence, the principal component analysis (PCA)- based feature reduction approachis deployed here. Finally, the dimensional concentrated features are fed as the input to the proposed ensemble technique with “Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN)” with optimized Neural Network (NN) as the final classifier. Results: An elaborative analyses as well as discussion have been provided by concerning the parameters, like evaluation metrics, year of publication, accuracy, implementation tool, and utilized datasets obtained by various techniques. Conclusions: From the experiment outcomes, it is proved that the accuracy of the proposed work with the proposed feature set is 5, 42.85, and 10% superior to the performance with other feature sets like central tendency + dispersion feature, central tendency qualitative variation, and dispersion qualitative variation, respectively. Results: Finally, the comparative evaluation shows that the presented work is appropriate for heart disease prediction as it has high accuracy than the traditional works.