AN INTELLIGENT HYBRID SYSTEM FRAMEWORK FOR CARDIOVASCULAR DISEASE PREDICTION EMPLOYING MACHINE LEARNING TECHNIQUES

Abstract

One of the most deadly diseases in the world, heart disease significantly affects people's life. It makes reference to the heart's inability to sufficiently pump blood to various body parts. Both preventing and treating heart failure depend on an accurate and timely identification of cardiac illness. It has long been believed that medical histories are unreliable for diagnosing heart issues. However, noninvasive techniques such as machine learning have proven to be reliable and effective in distinguishing between those with heart disease and those in good health. Using a dataset on heart illness, we created a machine learning-based diagnostic system for heart disease prediction in this proposed research. Seven well-known machine learning algorithms, three feature selection methods, seven classifier performance evaluation criteria, and a cross-validation approach were all employed. Among these requirements are execution speed, specificity, sensitivity, Matthews' correlation coefficient, and classification accuracy. Our proposed approach successfully distinguishes and categorises individuals with heart disease from healthy individuals. We also calculated the receiver operating characteristic curves and the area under the curves for each classifier. This paper covers in detail all classifiers, feature selection algorithms, preprocessing techniques, validation techniques, and metrics for evaluating the performance of the classifiers. Three feature selection methods, seven well-known machine learning algorithms, and a cross-validation approach were employed. In addition, we evaluated the classifiers' performance using seven criteria: classification accuracy, specificity, sensitivity, execution time, and Matthews' correlation coefficient. The proposed method effectively identifies and categorises people with heart disease when compared to healthy individuals. We also calculated the receiver operating characteristic curves and the area under the curves for each classifier. This paper covers all classifiers, feature selection algorithms, preprocessing techniques, validation processes, and metrics for evaluating classifier performance in detail.