Optimization heart disease prediction using independent component analysis and support vector machine

Abstract

Prediction models play a crucial role in early detection and intervention for cardiac diseases. However, their effectiveness is often hindered by limitations inherent in current methodologies. This paper proposes a novel approach to address these challenges by integrating Independent Component Analysis (ICA) with the Support Vector Machine (SVM) technique. Utilizing a comprehensive Cleveland dataset, our model achieves notable performance metrics, including an accuracy of 90.16%, an Area Under the Curve (AUC) of 96.66%, precision of 90.02%, recall of 90.00%, F1-score of 90.00%, and a minimal log loss of 3.54. Our methodology not only surpasses previous methodologies through extensive comparative analysis but also addresses common constraints identified in existing literature. These limitations encompass insufficient feature representation, overfitting, and a lack of proactive intervention strategies. By amalgamating ICA with SVM, our model enhances feature extraction, mitigates overfitting, and facilitates proactive diagnosis and intervention in individuals suspected of having heart disease. This study underscores the importance of mitigating current literature limitations and underscores the potential of integrating contemporary machine-learning techniques to advance prediction models for heart disease.