Data Sheet 1_Development and validation of a LASSO-selected machine learning prediction pipeline for predicting coronary artery calcification in a health check-up population.pdf

We developed and calibrated a parsimonious, interpretable machine learning model using routine health check-up variables to identify CAC-positive individuals suitable for early preventive lifestyle counseling.</p>Methods<p>A total of 4,465 participants were randomly divided into a training set (n = 3,125) and a testing set (n = 1,340) using stratified sampling based on CAC status.

Candidate predictors included demographic characteristics, clinical history, anthropometric measurements, blood pressure, and laboratory indices. Least absolute shrinkage and selection operator (LASSO) regression was applied in the training set for feature selection, followed by the construction of a class-weighted and probability-calibrated logistic regression model. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, the Hosmer-Lemeshow test, decision curve analysis (DCA), and SHAP interpretation.</p>Results<p>LASSO selected 13 predictors: sex, age, pulse, systolic blood pressure (SBP), diastolic blood pressure (DBP), hypertension (HTN), diabetes mellitus (DM), low-density lipoprotein cholesterol (LDL-C), serum creatinine (Scr), serum uric acid (SUA), non-alcoholic fatty liver disease (NAFLD), smoking, and drinking.