A Stacked Ensemble Multi-Label Model for Predicting Co-Occurring Microvascular and Macrovascular Complications in...

Type 2 diabetes mellitus (T2DM) leads to severe microvascular and macrovascular complications. Traditional single-label prediction models fail to capture their co-occurring nature. This study develops a stacked ensemble multi-label framework that integrates established machine learning techniques into a unified and clinically interpretable approach for the joint prediction of diabetes complications.

In a retrospective study of 965 T2DM patients, complications were aggregated into microvascular (retinopathy, nephropathy, and neuropathy) and macrovascular (cardiovascular, cerebrovascular) categories. A class-weighted stacking ensemble integrated three base models-Random Forest, Light GBM, and Cat Boost-within Binary Relevance (BR) and Classifier Chain (CC) frameworks, with a multi-output logistic regression meta-learner. Performance was evaluated via 5-fold cross-validation (Hamming Loss, F1-score, area under the curve [AUC]). SHapley Additive exPlanations (SHAP) analysis elucidated risk factors.

The Stacking-CC model achieved superior performance, with an overall F1-score of 0.752 ± 0.049 and AUC of 0.857 ± 0.032. A moderate correlation (r = 0.35) between complications validated the multi-label approach. SHAP analysis revealed distinct risk profiles: macrovascular complications were strongly associated with LDL cholesterol and diastolic blood pressure, while microvascular complications were linked to drug addiction, fasting blood sugar, and HDL. Conventional factors like age and BMI showed minimal importance.

The Stacking-CC framework effectively models co-occurring diabetic complications with high accuracy and interpretability. By delineating distinct risk hierarchies, it enables the development of targeted, complication-specific management strategies.