CARDIOPREDICT: INTELLIGENT RISK ASSESSMENT OF CHRONIC HEART FAILURE USING DEEP LEARNING

Abstract

Chronic Heart Failure (CHF) is a progressive cardiovascular condition that poses a significant global health challenge, contributing to high morbidity and mortality rates. Early detection and risk assessment are critical for effective intervention and management. This study proposes CardioPredict, an intelligent deep learning framework for predicting the risk of CHF using patient demographic, clinical, and physiological data. The system employs convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to analyze complex temporal and non-linear patterns within the data. Data preprocessing, normalization, and feature selection are applied to enhance model accuracy and efficiency. Experimental results indicate that CardioPredict achieves high predictive performance in terms of accuracy, sensitivity, specificity, and F1-score, enabling healthcare professionals to make informed decisions for early intervention, personalized treatment, and improved patient outcomes