DEEP LEARNING–BASED CLINICAL DECISION SUPPORT SYSTEM FOR HEART DISEASE PREDICTION USING ANN, CNN AND RNN–LSTM

Abstract

Heart disease is one of the most critical health challenges worldwide, and early diagnosis is essential for improving patient survival rates. This project presents a heart disease prediction system developed using deep learning algorithms such as Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN) and combination of  Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks for effective classification of cardiovascular conditions. The system processes clinical and physiological patient data through data cleaning, normalization, and feature optimization before applying the learning models. These deep learning models are trained to identify complex patterns in medical data and provide reliable predictions. The proposed approach enhances diagnostic accuracy, reduces manual analysis errors, and supports healthcare professionals in making timely and informed decisions. The experimental results confirm that deep learning–based models achieve better performance than traditional machine learning methods, making the system suitable for real-world clinical applications.