Comparative Study of Categorical and Binary Heart Disease Classification Using Neural Networks

Heart disease is a prevalent and life-threatening condition that demands accurate and timely diagnosis for effective treatment. This paper conducts a comparative study on the application of neural networks for heart disease classification, specifically investigating the differences between categorical and binary classification approaches. Utilizing a diverse dataset, two distinct neural network architectures are implemented and evaluated, each tailored to optimize performance for either categorical or binary representation of heart disease. The study explores key performance metrics such as accuracy, sensitivity, specificity, precision, and F1 score. Insights gained from this comparative analysis elucidate the strengths and limitations of neural networks in different classification scenarios. The findings contribute valuable knowledge to the field of medical diagnostics, guiding future research toward more effective and transparent neural network models for heart disease classification.