Applying Machine Learning with Tree Ensemble Methods and SHAP Values based on Routine Circulating Biomarkers to Detect Left Atrial Morphological and Functional Remodeling in Hypertension

Abstract

Introduction: Hypertension induces left atrial (LA) dysfunction and stiffness. Machine learning (ML) has been increasingly used in clinical diagnosis and prognosis prediction. To detect LA stiffness using ML with tree ensemble methods and SHAP values based on clinical biomarkers which were routinely measured in hypertension. Methods: 351 hypertensive patients were enrolled and measured LA volume (LAV) using the biplane modified Simpson’s method and LA reservoir strain (LAS-S) using 2D speckle-tracking echocardiography. The LA stiffness index (LASI) was defined as the ratio of E/eʹ to LAS-S. Four tree-based ML algorithms, including XGBoost, GBDT, Random Forest (RF), and LightGBM were used to discriminate the increased LASI (≥0.29) and LAV index (LAVI) ( ≥ 28 mL/m2) based on the routine circulating biomarkers including 38 features. We also used the SHAP values to evaluate features importance and interactions. Results: The top 20 selected variables were used as inputs for four ML models, GBDT presented the highest AUC/ROC (0.85, 95% CI 0.70-0.94) for predicting LASI, and RF model exhibited the best AUC/ROC (0.75, CI 0.57-0.92) for predicting LAVI. SHAP summary plot was applied on GBDT or RF model to identify feature contribution to LA stiffness and LA enlargement, and SHAP also revealed the interactions between variables. Conclusions: tree-based ML models with the SHAP method combining routine circulating biomarkers predicted LA stiffness with high accuracy. ML models can be useful to screen hypertensive patients with preclinical cardiac TOD, in order to improve personalized medical care at low cost.