A machine learning approach to predict positive coronary artery calcium scores in individuals with diabetes: a cross-sectional analysis of ELSA-Brasil baseline data.

Abstract

It is unclear who benefits the most from atherosclerotic cardiovascular disease (ASCVD) screening imaging. This study aimed to identify features associated with positive coronary artery calcium scores (CACS) in individuals with diabetes using machine learning (ML) techniques. ELSA-Brasil is a cohort study with 15,105 participants aged 35 to 74 years in six Brazilian cities. We analyzed 25 sociodemographic, medical history, symptom-related, and laboratory variables from 585 participants from the São Paulo investigation center with CACS data and no overt cardiovascular disease at baseline. We used six ML algorithms to build models to identify individuals with positive CACS. Feature importance was determined by SHapley Additive exPlanations (SHAP) values. The best performer ML algorithm was the XGBoost Classifier (accuracy: 94.8%). Age (SHAP: 0.220), systolic blood pressure (SHAP: 0.102), and body mass index (SHAP: 0.075) were the most important variables to identify ASCVD in individuals with diabetes in XGBoost models. Considering all ML models in our analysis, age, systolic blood pressure, and sex were frequently influential variables. We obtained high accuracy with our best model, using information generally present in current clinical practice. ML models may help clinicians select patients with characteristics most probably associated with a positive CAC. Age, systolic blood pressure, body mass index, and sex may be useful markers to identify those at higher risk for subclinical ASCVD.