International Journal of COPD
Development and Real-World Evaluation of an Interpretable Machine Learning Framework for Predicting COPD-Related Healthcare Costs
By Yi-Li Chen, Guan-Heng Liu, Chin-Ling Li and 4 others.
Abstract
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.
