English

Abstract

Early heart Disease prediction can reduce heart illness, which is a leading source of worldwide death. Unhealthy eating, mental stress, genetics, a sedentary lifestyle, and other factors have all contributed to the development of heart disease. Angina pectoris, dilated cardiomyopathy, stroke, and heart disease are most commonly caused by congestive heart failure. Moreover, a precise heart disease prognosis is necessary for effective cardiac treatment. In clinical machine learning, it is dangerously challenging to predict heart disease. This paper proposes a method based on Jellyfish Search Optimization (JSO) and enabled Deep Residual Networks (DRN) to improve heart disease prediction. The input dataset is subjected to the preprocessing step, which utilizes missing data imputation and Z- score Normalization. Consequently, the pre-processed output is fed to the feature selection stage, wherein features are selected by Kumar- Hassebrook similarity. At last, in the heart disease diagnosis phase, where DRN makes the heart disease identification, DRN is trained using the proposed JSO. Moreover, the proposed DRN_JSO has effectively delivered better performance parameters with high accuracy of 84.65% F1_score of 83. 65%, Matthews’s correlation coefficient (MCC) of 85.65% and a True Positive Rate (TPR) of 82.65%.