Fig. 3

Comprehensive ML Framework Integrating Boruta Feature Selection, Correlation Analysis, and Model Performance Evaluation with AUROC and Residual Distribution. A. Important feature variable filtering based on Boruta algorithm. B. Assessment of feature correlations between important variables. C. The AUROC curve of the respective residual distribution. D. The AUROC curve of the respective residual distribution. Abbreviations: DBP, Diastolic Blood Pressure; SBP, Systolic Blood Pressure; BMI: Body Mass Index; RF, Random Forest; GLM: Generalized Linear Model; KNN, K-Nearest Neighbor; SVM, Support Vector Machine; GBM, Gradient Boosting Machine; NNET, Neural Network; DT, Decision Tree; LASSO, Least Absolute Shrinkage and Selection Operator; SHAP, SHapley Additive exPlanations; AUROC, Area Under the Receiver Operating Characteristic Curve; ML, Machine Learning