Published Paper Details:

According to the World Health Organization, coronary heart disease and all related diseases account for 18.6 million deaths worldwide each year. The disease's early detection and study could be important, and they might even hold the key to its ultimate cure. Because the main goal is to identify the illness at an early stage, the majority of scientists and academics concentrate on machine learning techniques that can accurately identify illnesses from large and complex data sets. These techniques then offer medicinal assistance. In order to identify cardiac illnesses early on and prevent outcomes, this research employs a variety of machine learning algorithms, including KNN Decision Tree (DT), Logistic Regression, SVM, Random Forest (RF), and Nave Bayes (NB). The article's main goal is to create a system that is entirely artificial intelligence-based and uses machine learning to identify heart diseases. We outline a technique for anticipating the progression of cardiac disease using device learning. This service, which is crucial given its estimated 88% accuracy rate over educational statistics, requires data analysis.