Towards Personalized Lipid Management: Predicting Statins Therapy Eligibility through Machine Learning Models

Abstract

Cardiovascular diseases are among the leading causes of death worldwide and a major contributor to the deterioration of quality of life. Therefore, it is highly beneficial to follow the clinical guidelines and recommendations for preventing and treating cardiovascular diseases at their early stages. Cholesterol-lowering drugs such as Statins are considered first-line medications for the prevention of atherosclerotic cardiovascular diseases (ASCVD). However, it is not easy to determine patients’ eligibility for statin therapy. In this work, we built efficient and accurate prediction models based on several machine learning algorithms for predicting patients' eligibility for Statins using several cardiovascular disease risk factors. The results indicated that the gradient boosting classifier achieved 95.6% accuracy and 99.0% area under the curve in predicting patients' eligibility for statin therapy. Other simpler but more explainable algorithms such as decision tree and logistic regression also demonstrated good performance.