Heart Disease Prediction Using Deep Learning

Abstract

In this paper, we undertake a detailed review of the integration of state-of-the-art deep learning models to reliably forecast heart illness. In order to obtain a trustworthy and accurate diagnostic level, our work comprises a comprehensive investigation of various features, inventive preprocessing approaches, and sophisticated model structures. We demonstrate the remarkable effectiveness of deep learning paradigms in significantly improving the prediction accuracy  of heart disease via extensive testing and analysis, therefore highlighting the revolutionary potential of contemporary computational techniques in the field of medical diagnostics