OPTIMIZATION HEART DISEASE PREDICATION USING MACHINE LEARNING

A chronic illness that affects millions of people globally is heart failure. In order to predict early heart failure health status and take the necessary activities to address this widespread problem, an effective machine learning-based approach is required. Exercise is becoming recognized as an effective adjunct therapy for controlling heart failure, even if medicine remains the main course of treatment. In this work, we developed an approach to enhance heart failure detection based on patient health parameter data involving machine learning. Our work helps improve heart failure detection at its early stages to save patients’ lives. We employed nine machine learning based algorithms for comparison and proposed a novel Principal Component Heart Failure (PCHF) feature engineering technique to select the most prominent features to enhance performance. We optimized the proposed PCHF mechanism by creating a new feature set as an innovation to achieve the highest accuracy scores. The newly created dataset is based on the eight best-fit features. We conducted extensive experiments to assess the efficiency of several algorithms.