Explainable Machine Learning for Cardiovascular Disease Prediction Using BRFSS Health Indicators

Abstract

Cardiovascular disease remains a major public health burden and is associated with demographic, behavioral, and chronic-health characteristics. This study evaluates explainable machine learning for cross-sectional classification of self-reported cardiovascular disease status using the 2020 CDC Behavioral Risk Factor Surveillance System (BRFSS)-derived Personal Key Indicators of Heart Disease dataset. After removing 18,078 exact duplicate feature-target rows, 301,717 records and 17 predictor variables were retained. The target variable, HeartDisease (Yes/No), indicates whether a respondent had ever been diagnosed with cardiovascular disease or had experienced a heart attack; therefore, the task is classification of prevalent disease status rather than longitudinal prediction of future disease incidence. Six machine learning families were compared using stratified train-test splitting, 5-fold cross-validation, cost-sensitive learning, and hyperparameter tuning. The tuned XGBoost model achieved the highest test-set F1-score of 0.3939 (approximate 95% CI: 0.384–0.403) and a ROC-AUC of 0.8362 (approximate 95% CI: 0.829–0.843). SHAP analysis showed that age category, self-rated general health, sex, smoking status, and body mass index contributed most strongly to the model outputs. These SHAP values describe model dependence and should not be interpreted as causal effects or as novel epidemiological risk-factor discoveries. A prototype business intelligence dashboard was developed for research-oriented communication of model performance and feature contributions. The results support transparent classification of self-reported cardiovascular disease status for analytical and preliminary screening contexts, not future-risk forecasting or direct clinical diagnosis.