Development and validation of a LASSO-selected machine learning prediction pipeline for predicting coronary artery calcification in a health check-up population.

Background: Coronary artery calcification (CAC) screening in young, asymptomatic populations is clinically valuable but constrained by CT radiation and cost. 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. Methods: A total of 4,465 participants were randomly divided into a training set ( Results: 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. The final model achieved an AUC of 0.816 in the training set and 0.771 (95% CI, 0.713-0.824) in the testing set. At the optimal classification threshold, the testing-set sensitivity, specificity, accuracy, PPV, NPV, F1 score, Brier score, and AUPRC were 0.726, 0.689, 0.691, 0.102, 0.981, 0.179, 0.042, and 0.147, respectively. Calibration was acceptable in both cohorts. DCA suggested positive net benefit within a low threshold probability range relevant to preventive counseling and further cardiovascular risk assessment. SHAP analysis identified age, sex, hypertension, pulse, and NAFLD as the most influential predictors. Conclusions: The LASSO-selected and calibrated logistic regression model demonstrated acceptable discrimination and calibration for CAC positivity in an internally validated health check-up cohort. Its potential use should be interpreted as preliminary risk stratification rather than clinical implementation, and independent multicenter external validation is required before the model can be recommended for routine clinical use.