Developing Machine Learning Models for Cardiovascular Disease Prediction

Abstract

Cardiovascular diseases (CVDs) are a range of heart and blood vessel problems leading to death worldwide. It is critical to discover cardiac diseases as early as feasible in order to extend one's life expectancy. Machine learning is an efficacious method for predicting the presence of severe diseases and the risk they cause to patients. In this paper, five machine learning algorithms namely Logistic Regression, Random Forests, K-Nearest Neighbor, Decision Trees, and Support Vector Machines were executed to predict the risk of cardiovascular diseases. These results can then be used to assist the doctors in identifying the patients with a higher risk of heart failure to ensure timely treatment.