Development and Real-World Evaluation of an Interpretable Machine Learning Framework for Predicting COPD-Related Healthcare Costs.

Background: Chronic obstructive pulmonary disease (COPD) imposes a substantial economic burden. While machine learning (ML) enables data-driven cost prediction, real-world benchmarking comparing algorithms, data-splitting strategies, and feature optimization on continuous, skewed healthcare expenditures remains limited. Objective: To develop an interpretable ML framework for predicting annual COPD-related healthcare expenditures, assessing the impact of algorithm selection, train-test split ratios, and feature optimization. Methods: We analyzed a retrospective cohort of 396 COPD patients from a Taiwanese medical center. Annual healthcare costs were log-transformed and standardized due to extreme skewness. Five ML regression algorithms were benchmarked across four train-test split ratios. Recursive feature elimination with cross-validation (RFECV) applied within training sets identified an optimal predictor subset. Model interpretability was assessed using SHapley Additive exPlanations (SHAP). Performance was evaluated using R-squared (R Results: The 80:20 split yielded the most stable generalization performance. Gradient Boosting achieved the highest accuracy (test R Conclusion: Gradient Boosting provides robust predictive performance for COPD healthcare costs. A parsimonious six-feature set enhances interpretability without compromising accuracy. This explainable ML framework supports cost-aware healthcare management, pending future multi-center validation.