Deep Learning–Based Heart Disease Detection System

Abstract

Heart disease remains one of the leading causes of mortality worldwide, emphasizing the need for early and accurate diagnosis. Traditional diagnostic methods depend heavily on clinical expertise and manual analysis, which may result in delayed or inaccurate predictions. In recent years, deep learning has emerged as a powerful tool for medical diagnosis due to its ability to automatically learn complex patterns from large datasets. This paper presents a deep learning–based approach for heart disease detection using clinical patient data. The proposed system employs a deep neural network to classify patients as having heart disease or not. Experimental results show that the proposed model achieves higher accuracy compared to traditional machine learning techniques, demonstrating its effectiveness in assisting clinical decision-making.