Predicting Cardiovascular Risk Using Machine Learning Algorithms

Abstract

CVDs are among the most preventable diseases globally, yet they continue to cause more than one in three deaths worldwide. Objectives This study investigates the use of machine learning (ML) techniques for the prediction of cardiovascular risk from basic clinical and lifestyle factors. The analysis used a publicly available data set of medical records of 70,000 patients that included variables on the patients' age, blood pressure, cholesterol levels, glucose levels, and lifestyle, including smoking and exercise. Deep analysis on the preprocessing data, we used Recursive Feature Elimination (RFE) with Random Forest estimator to explore key predictors. Multiple ML models (Logistic Regression, Support Vector Machine, Random Forest and Gradient Boosting) were trained by 5-fold cross validation for hyperparameter tuning. The accuracy, precision, recall, F1-score, and AUC-ROC metrics were used to assess the performance of the models. Based on the results, Gradient Boosting was the most accurate and overall, the most robust of the algorithms considered. Implication of all the available evidence: The current knowledge synthesis demonstrates a potential role for ML to improve clinical decision-making related to cardiovascular risk assessment and stratification for early prevention as well as a strong need for multi-centre trials to affirm these findings.

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