A Data-Driven Model to Predict Regular Strength Exercise Patterns in Patients With Chronic Obstructive Pulmonary Disease: A Secondary Analysis

Abstract

Regular exercise plays a crucial role in the management of chronic obstructive pulmonary disease (COPD), yet many patients struggle with adherence to such health-promoting behaviors. This study aimed to develop and evaluate machine learning and deep learning models to predict adherence to regular strength exercise among individuals with COPD using data from the Korean National Health and Nutrition Examination Survey (KNHANES) collected between 2007 and 2019. A total of 5,060 patients with COPD were identified based on established clinical criteria, and 25 key variables were selected for model development. We compared the performance of four machine learning models (logistic regression, support vector machine, decision tree, XGBoost) and three deep learning models (multi-layer perceptron, convolutional neural network, FT-Transformer). Model performance was assessed using precision, recall, F1 score, and weighted F1 score. The FT-Transformer model outperformed other models, achieving a precision of 0.64, a recall of 0.68, an F1 score of 0.64, and a weighted F1 score of 0.71. Shapley Additive Explanations (SHAP) were used to interpret model predictions and revealed that weekly walking frequency, education level, and weight management were key predictors of adherence. These findings demonstrate the potential of AI-driven models to provide actionable insights into patient behavior and support personalized interventions. Further research using longitudinal data is recommended to improve predictive performance and validate model applicability in clinical settings.