Machine learning prediction of physician-assigned treatment categories from pre-procedural electronic medical record data in cardiac patients: A multiclass classification study.

Cardiovascular diseases (CVDs) remain a leading source of morbidity, mortality, and healthcare burden worldwide. In patients with coronary artery disease (CAD), choosing among medical therapy, percutaneous coronary intervention (PCI), and coronary artery bypass grafting (CABG) is clinically complex and depends on multiple pre-procedural factors. In this retrospective multicenter registry study, we developed a multiclass machine learning model to predict physician-assigned treatment categories rather than clinically optimal treatment. The analytic cohort included 2,682 unique patient records from the BioArc Clinical Registry, with class imbalance across medical therapy (n = 1,634), PCI (n = 817), and surgery (n = 231). Semi-structured JSON records were parsed to extract pre-decision demographic, clinical, symptom-related, and risk-factor variables. Missing data handling, encoding, SMOTE, recursive feature elimination (RFE), and hyperparameter tuning were performed within a nested stratified cross-validation workflow to reduce dependence on a single data split and to limit information leakage. The final XGBoost workflow retained 18 predictors after RFE. Across the outer folds of nested cross-validation, the model achieved an overall accuracy of 82.3% ± 1.5% (95% CI: 80.8%-83.8%) and a macro F1-score of 0.775 ± 0.018 (95% CI: 0.757-0.793), with one-vs-rest AUC values of 0.78 ± 0.02, 0.75 ± 0.02, and 0.80 ± 0.03 for medical therapy, PCI, and surgery, respectively. Class-specific performance and confusion patterns were summarized from outer-fold predictions. Model interpretation using SHAP and partial dependence plots identified age, systolic blood pressure, symptom-related features, and opium consumption as influential predictors in this dataset. These findings indicate that pre-procedural EMR data contain signal related to observed treatment-assignment patterns; however, the model does not identify the optimal treatment or demonstrate improved clinical outcomes. External validation, external calibration, and prospective outcome-based evaluation are required before clinical deployment.