Developing and validating machine learning-based prediction models for frailty occurrence in those with chronic obstructive pulmonary disease

作者：Yong Chen, Yonglin Yu, Dongmei Yang, Wenbo Zhang, Vasileios Kouritas, Xiaoju Chen · 发表于：Journal of Thoracic Disease · 年份：2024 · DOI：10.21037/jtd-24-416 · 被引用次数：22 · 研究领域：Frailty in Older Adults、Chronic Disease Management Strategies、Chronic Obstructive Pulmonary Disease (COPD) Research

Background: Frailty is a medical syndrome caused by multiple factors, characterized by decreased strength, endurance, and diminished physiological function, resulting in increased susceptibility to dependence and/or death. Patients with chronic obstructive pulmonary disease (COPD) tend to be more vulnerable to frailty due to their physical and psychological burdens. Therefore, the aim of this study was to develop a reliable and accurate vulnerability risk prediction model for frailty in patients with COPD in order to improve the identification and prediction of patient frailty. The specific objectives of this study were to determine the prevalence of frailty in patients with COPD and develop a prediction model and evaluate its predictive power. Methods: Clinical information was analyzed using data from the 2018 China Health and Retirement Longitudinal Study (CHARLS) database, and 34 indicators, including behavioral factors, health status, mental health parameters, and various sociodemographic variables, were examined in the study. The adaptive synthetic sampling technique was used for unbalanced data. Three methods, ridge regressor, extreme gradient boosting (XGBoost) classifier, and random forest (RF) regressor, were used to filter predictors. Seven machine learning (ML) techniques including logistic regression (LR), support vector machines (SVM), multilayer perceptron, light gradient-boosting machine, XGBoost, RF, and K-nearest neighbors were used to analyze and determine t...