Detection of Heart Disease Using Machine Learning

Heart disease is one of the key causes of death worldwide, and it is important to detect in its early stage which helps in halting its progression. Because of its superiority in pattern identification and categorization, machine learning (ML) supports decision making and risk assessment from the massive amount of data produced by the healthcare industry on cardiac disorders. This research paper examines the ML techniques which are being used to detect heart diseases. This study made use of a dataset from a cardiovascular study of residents of Framingham, Massachusetts. The classification goal is to predict if the patient will develop coronary heart disease in the next ten years. The goal of this paper is to present the comparison among six different machine learning models namely Logistic Regression, Support Vector Machine, Decision Tree, AdaBoost, KNN and Random Forest to predict the 10-year risk of coronary heart disease. The paper presents the performance comparison evaluation on the basis of accuracy, precision, recall and F1-Score.