Abstract:

Objective To develop a machine learning-based model with good generalization ability for pulmonary function assessment in elderly patients with impaired pulmonary function. Methods A total of 7720 elderly patients undergoing pulmonary function testing (PFT) in Guangdong Provincial People's Hospital between September 1, 2020 and December 31, 2024 were enrolled. After variable screening by univariate analysis and LASSO regression, the patients were randomly assigned in an 8:2 ratio to the training set (n=6176) and test set (n=1544). Five regression models were constructed for predicting forced vital capacity (FVC) and forced expiratory volume in one second (FEV₁), and their performance was evaluated based on Pearson correlation coefficient (r) and intraclass correlation coefficient (ICC). Feature contributions were interpreted using Shapley additive explanations, and the classification performance of the models for high-risk populations was assessed. Results XGBoost showed the best predictive performance in the elderly patients (r=0.733 for FVC and 0.711 for FEV₁; ICC=0.694 for FVC_{and 0.668 for FEV1). Male gender and a younger age showed positive effects on pulmonary function, while female gender and an advanced age showed negative effects. The optimal cut-off values of FVC% and FEV1% were 82.93% and 85.46%, with AUC of 0.700 and 0.706, respectively. Conclusion The XGBoost-based model developed in this study is simple and reliable with good generalization ability for pulmonary function assessment in elderly patients.}

Key words: machine learning, elderly, pulmonary function, model evaluation, extreme gradient boosting