基于机器学习和COPD-SQ问卷的COPD风险预测模型构建OA

Objective To construct and evaluate various machine learning models for predicting the risk of chronic obstructive pulmonary disease(COPD)in individuals,thereby providing data support for early screening and intervention.Methods A total of 823 subjects were selected for this study,comprising 142 individuals in the high-risk group for COPD and 681 individuals in the low-risk group.Data collected included demographic charac-teristics,smoking history,symptoms(such as cough and shortness of breath),and scores from the Chronic ob-structive pulmonary disease screening questionnaire.Four machine learning algorithms—Logistic regression,ran-dom forest,support vector machine,and XGBoost—were utilized to construct risk prediction models.The perfor-mance of these models was assessed using 5-fold cross-validation,with evaluation metrics including accuracy,pre-cision,recall,F1-score,area under the receiver operating characteristic curve(AUC),and average precision(AP).Furthermore,a feature importance analysis was performed.Results The Logistic Regression model exhib-ited superior performance,achieving an AUC of 0.982 and an AP of 0.939.This was closely followed by the Ran-dom Forest model,which recorded an AUC of 0.975 and an AP of 0.890.Feature importance analysis revealed that smoking history,symptoms of shortness of breath,and body weight were significant predictors.All models demonstrated robust performance in identifying low-risk populations;however,variations were observed in their ef-ficacy in identifying high-risk populations.Conclusion Machine learning models have proven effective in identify-ing individuals at high risk for COPD.Among these,the Logistic regression model exhibits the best overall perfor-mance,efficiently identifying high-risk populations and serving as a valuable clinical auxiliary screening tool.Vari-ous models,each with distinct performance characteristics,are suited to different clinical screening scenarios,thereby offering targeted decision-making support for the establishment of a hierarchical and intelligent COPD screening pathway.