Enhancing Coronary Heart Disease Risk Prediction with Machine Learning and Deep Learning

• Published in: International journal for research in applied science and engineering technology Vol. 12; no. 4; pp. 5367 - 5371
• Main Authors: Padmaja, Mrs. P S H R, Sudarshan, Velpula, Babu, K Sudheer, Lakshman, M M Krishna, Akhilandeswari, K
• Format: Journal Article
• Language: English
• Published: 30.04.2024
• ISSN: 2321-9653; 2321-9653
• DOI: 10.22214/ijraset.2024.61185

Coronary artery disease is the world's most common cardiac problem today. Humans face a major situation that must be properly diagnosed. Plaque formation in the coronary arteries, which is largely composed of calcium, fibrin, and cholesterol, produces a partial blockage of blood flow over time, eventually leading to coronary artery disease. Coronary arteries help the heart circulate oxygen-rich blood throughout the body. On the other hand, early detection and precise diagnosis will decrease the likelihood of developing it. This study looks into the possible use of deep learning algorithms to predict cardiac disease in its early stages. The primary goal of this study is to precisely determine whether or not an individual has cardiac abnormalities. Early-stage prediction can be implemented with a range of machine learning algorithms and deep learning techniques. It can be applied to data mining, decision trees, Naive Bayes, and artificial neural networks (ANNs). The ANN Model will be employed in the research.