Revolutionizing Heart Disease Prediction and Identification with Machine Learning and FFT-Based Recommender System

The escalating prevalence of heart diseases necessitates innovative methods for early detection. This research introduced the Heart Disease Fast Fourier Transform Prediction (HDFFTP) approach, a novel method combining Fast Fourier Transform (FFT) with advanced machine learning to analyze time-series cardiac data. By transitioning from a time-based to a frequency-based view via FFT, the system unveils nuanced patterns integral to heart health. Upon implementing the XGBoost model, an accuracy of 93.5%, precision of 91.2%, and a commendable recall of 95.4% were achieved. Notably, the model spotlighted specific frequency components, like 1.2 Hz, as crucial indicators of cardiac conditions. Such insights, derived from the analysis of feature importance, underscore the potential of FFT in enhancing diagnostic precision. In summation, the HDFFTP approach provides a promising avenue for heart disease prediction, bridging computational excellence with clinical utility to foster timely interventions and improve patient outcomes.