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Heart failure is a chronic disease affecting millions worldwide. An efficient machine learning-based technique is needed to predict heart failure health status early and take necessary actions to overcome this worldwide issue. While medication is the primary treatment, exercise is increasingly recognized as an effective adjunct therapy in managing heart failure. In this study, we developed an approach to enhance heart failure detection based on patient health parameter data involving machine learning. Our study 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 …