Explainable Lightweight AI for the Identification of Right-Sided Cardiac Dysfunction in a Saudi Arabian Diabetic Cohort

Background: Tricuspid valve dysfunction is historically underdiagnosed, and right-sided cardiac abnormalities are clinically important in high-risk diabetic populations. Diabetes may promote right-sided dysfunction through cardiometabolic remodeling, diastolic dysfunction, and elevated pulmonary pressures. This study introduces an explainable, lightweight artificial intelligence framework to infer extreme right heart phenotypes from left-sided echocardiographic and systemic clinical markers. Methods: We retrospectively analyzed an existing clinical dataset of approximately 370 Saudi Arabian individuals with diabetes mellitus. Seven baseline machine learning classifiers were evaluated using leak-aware preprocessing. To reduce optimism bias, models were validated with a True Nested Cross-Validation protocol. Probabilistic calibration, parameter reduction, and computational efficiency were prioritized for clinical triage, and SHapley Additive exPlanations (SHAP) supported transparent decision-making. Results: Random Forest was selected for its balance of discrimination and calibration. Under True Nested Cross-Validation, it achieved an AUC-ROC of 0.8602, AUC-PR of 0.8343, accuracy of 0.8075, sensitivity of 0.7733, specificity of 0.8239, Brier Score of 0.1281, Calibration Slope of 1.1720, and Observed/Expected Ratio of 0.8522. Feature ablation indicated a holistic cardiometabolic severity signal, with left atrial dimensions, diastolic dysfunction grade, and diuretic use as primary predictors. The model required 205.00 kilobytes of storage and 14.1956 milliseconds for inference. Conclusions: Left-sided cardiac markers combined with systemic indicators can flag concurrent severe right-sided abnormalities. This lightweight framework is a promising triage-oriented screening prototype for prioritizing echocardiographic assessment in high-risk diabetic patients during routine clinical visits without requiring specialized hardware; however, until prospective external validation is completed, this study must strictly be viewed as hypothesis-generating.