Prediction of Cardiovascular Diseases Using Machine Learning

Abstract

A condition of the heart or blood vessels is referred to as cardiovascular disease (CVD). Globally, cardiovascular diseases (CVDs) constitute the primary cause of mortality. Coronary heart disease is among the most frequent cardiovascular conditions. Additional examples of coronary heart disease include aortic disease, peripheral artery disease, strokes, and decreased or obstructed blood supply to the heart muscle. Another important fact regarding cardiovascular disorders is that they are typically linked to an accumulation of fatty deposits within the arteries and a heightened risk of blood clots. In this work, I developed a cardiovascular disease prediction model using machine learning techniques, with accuracy rates of 92.62% and 89.05%, respectively, for the Stacking Classifier and k-Nearest Neighbors algorithms. This models are performed particularly well and demonstrating the potential utility of machine learning as a tool for cardiovascular disease. In addition to Random Forest and Decision Tree and I evaluated several other algorithms, including Support Vector Machine and Logistic Regression and an ensemble model using Voting Classifier. Here Support Vector Machine does not provide very good accuracy. Our findings show that Stacking Classifiers, KNN, machine learning have the potential to predict cardiovascular disease and imply that these technologies may prove beneficial in enhancing the disease's detection and management.