Predicting Cardiovascular Disease Using Simple Machine Learning Techniques

Abstract

Cardiovascular diseases (CVD) become a major health concern, which needs improved prediction models for intervention. In this study, this research explores the possibilities of using simple machine learning methods, including Logistic Regression, Decision Tree, and Random Forest, for predicting cardiovascular diseases effectively. Using a large dataset (308854 instances) containing demographic, binary, and numerical information, the research applied the above machine learning algorithms to develop predictive models. This study involved data preprocessing, including feature selection and handling duplicate values. This study then divides the dataset into two subsets: training set and testing set by 7:3 and 8:2. The Logistic Regression algorithm demonstrated good predictive performance, with an accuracy rate of 92%. Decision Tree exhibited a similar accuracy of 92%, while Random Forest underperformed both slightly with an accuracy of 91%.