A machine learning approach to conglomerate multi-domain features of cardiac aging

Owing to the breadth of complex and highly dimensional clinical data associated with ageing, integration of multiple health domains is needed towards determining cardiac outcomes of older adults. We designed a machine learning (ML) approach to conglomerate multi-domain data and identify determinants of cardiac function in older adults.

We applied a structured ML pipeline including data pre-processing, feature selection, and model development using Random Forest, Gradient Boosting, XGBoost, LightGBM, and support vector machine. Model performance was evaluated using stratified k-fold cross-validation and complementary discrimination metrics, including ROC–AUC, PR-AUC, balanced accuracy, sensitivity, and specificity. Feature importance was assessed using Random Forest (RF) importance and Shapley Additive exPlanations (SHAP), and the Tree-based Pipeline Optimization Tool (TPOT) was used for model optimization. The outcome was an impaired myocardial relaxation phenotype based on the mitral peak early-to-late diastolic filling velocity (E/A) ratio. The multi-domain dataset included demographic characteristics, clinical risk factors, physical activity, body composition, serum biomarkers, omics, and cardiac imaging, comprising 227 features from 984 older adults. Thirty key features were identified, mainly related to physical function and metabolomics. Using these features, the selected classifiers achieved ROC–AUC values above 0.79. XGBoost was retained as the primary tree-ensemble benchmark, with cross-validated ROC–AUC 0.8157 and test-set ROC–AUC 0.7658; TPOT was comparable (test-set ROC–AUC 0.7675). Higher XGBoost score was associated with death-or-admission events (HR 1.115, P = 0.029).

Multi-domain ML identified clinically interpretable signals associated with impaired myocardial relaxation in ageing and with clinical events.

ClinicalTrials.gov Identifier: NCT02791139.