A Survey on Early Heart Disease Prediction using Machine Learning Techniques

Cardiovascular diseases, often known as heartrelated disorders, or CVDs, have been leading cause of death worldwide in recent decades and are now most deadly illness, not just in India but globally as a whole. Therefore, in order to diagnose such diseases in time for appropriate treatment, a system that is dependable, accurate, and practical is required. Machine learning techniques and methodologies are utilized to a variety of medical datasets in an effort to automate analysis of large as well as complex data. Many researchers have recently utilized a variety of machine learning (ML) techniques to assist experts and health care business in diagnosing heart-related disorders. This review explores different models used for heart disease prediction, categorizes learning-based approaches, datasets, and configurations, and evaluates their performance metrics. Additionally, it identifies the most suitable techniques for specific types of heart diseases. As the availability of medical data continues to grow, ML-based methods are being widely adopted to enhance prediction accuracy. However, further research is necessary to improve the precision and reliability of heart disease diagnosis.