A Hybrid Explainable Ensemble Learning Framework for Health Risk Prediction

Early prediction of patients’ health risk is a crucial component of the safe and effective implementation of clinical triage and timely intervention. Health risk data in the real world often tends to be small in size and limited, with class imbalance, making the overall accuracy and transparency of Machine Learning (ML) models insufficient and more difficult to achieve. This paper proposes a hybrid explainable ensemble learning framework for multi-class health risk prediction, which is built based on a stacking architecture with three strong base learners, namely Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). The XGBoost is chosen as the meta-learner due to its ability to learn complex non-linear probability mapping from the base models and provide complementary signals for the prediction. A complete preprocessing pipeline is implemented, covering missing data handling, systematic encoding of categorical variables, and strict separation between training and test sets to ensure unbiased assessment. Experimental results show that the proposed framework achieved an accuracy of 97.5%, which exceeds the accuracy results of individual models, which are 96.0%, 95.5%, and 96.0% for RF, XGBoost, and LightGBM, respectively. Additionally, the proposed framework integrates the predictive performance with the interpretable clinical decision support and transparency using SHapley Additive exPlanations (SHAP) method. The SHAP values are used to provide global and local explanations for revealing the most influential features that drive each prediction.