An Automated Coronary Artery Disease Diagnosis System using Machine Learning

Abstract

Heart disease is the biggest cause of death worldwide. It is a complex process that requires experience and a high level of knowledge for medical professionals to forecast, thus it cannot be easily foreseen. Internet-based healthcare systems provide access to a large quantity of data. However, adequate data analysis techniques to uncover hidden correlations and patterns in data are lacking. A system that automates medical diagnosis would increase medical efficiency and decrease expenses. In this paper, to forecast the occurrence of heart disease, a dataset is collected from Kaggle. The objective is to extract heart disease-relevant patterns from the information using machine learning techniques for forecasting heart disease present in individuals. The highest value of accuracy is obtained on random forest and the value is 88.52%.