Abstract Body: Background: Cardiovascular diseases (CVDs) remain the leading cause of death globally, yet traditional risk tools underweight protective social and behavioral contexts that shape cardiovascular resilience. Explainable machine learning (ML) offers a path to quantify such protective factors while maintaining transparency for population health deployment.

Hypothesis: We hypothesize that explainable ML applied to a nationally representative survey can accurately distinguish adults without CVDs and identify a reproducible profile of protective social, behavioral, and clinical factors associated with lower disease prevalence.

Methods: We analyzed adults from the 2021 Behavioral Risk Factor Surveillance System (n=116,608); 11 candidate predictors spanned demographics/socioeconomics (age, sex, race/ethnicity, income, insurance), behaviors (smoking, alcohol use, fruit/vegetable intake), and clinical/mental health (diabetes, depressive disorder). Baselines included logistic regression, random forests, support vector machines, and XGBoost that underwent Optuna hyperparameter tuning with nested cross-validation. Discrimination (area under the receiver-operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC)), accuracy, and predictive values, were reported; interpretability used SHAP to quantify direction and magnitude of feature effects.

Results: CVD prevalence rose with age (1.3% at 18–24 years to 24.6% at ≥65 years) and was higher in men than women (17.2% vs 12.2%); prevalence was greater in Black non-Hispanic than Hispanic adults (16.7% vs 9.6%) and in lower- versus higher-income groups (23.6% for <$15,000 vs 7.5% for ≥$200,000). On held-out testing, the best XGBoost model achieved AUROC of 0.76 and AUPRC of 0.95, with high negative predictive value (90.49%) and substantially low positive predictive value (32.36%); cross-validation confirmed robustness (mean AUROC=0.76, high AUPRC=0.95) and competitive overall accuracy of 76.98±0.45 across five folds. SHAP analyses consistently highlighted protective profiles characterized by younger age, higher income, health insurance coverage, and absence of diabetes and depressive disorder; dietary variables contributed modestly.

Conclusions: Our explainable ML model identified protective profiles against CVDs, while achieving reliable performance. These results support prevention strategies that improve access to care, reduce metabolic risk, and motivate prospective validation.