Machine Learning-Assisted Diagnosis Model for Chronic Obstructive Pulmonary Disease

Yongfu Yu, Nannan Du, Zhongteng Zhang, Weihong Huang, Min Li
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Abstract

Chronic obstructive pulmonary disease (COPD) is a long-term, irreversible, and progressive respiratory disease that often leads to lung function decline. Pulmonary function tests (PFTs) provide valuable information for diagnosing COPD; however, they are underutilised in clinical practice, with only a subset of test values being used for decision making. The final clinical diagnosis requires combining PFT results with patient information, symptoms, and other tests, such as imaging and blood analysis. This study aims to comprehensively utilise all the testing information in PFTs to assist in the diagnosis of COPD. Various machine learning models, such as logistic regression, support vector machine (SVM), k-nearest neighbour (KNN), random forest, decision tree, and XGBoost, have been employed to establish COPD diagnosis assistance models. The XGBoost model, trained with features extracted by the group LASSO algorithm, achieved the best performance, with an area under the receiver operating characteristic curve (ROC) of 0.90, 88.6% accuracy, and 98.5% sensitivity. This model can assist doctors in the clinical diagnosis and early prediction of COPD.
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慢性阻塞性肺病的机器学习辅助诊断模型
慢性阻塞性肺病(COPD)是一种长期、不可逆、进行性的呼吸道疾病,常导致肺功能下降。肺功能测试(PFTs)为诊断COPD提供了有价值的信息;然而,它们在临床实践中没有得到充分利用,只有一部分测试值用于决策。最终的临床诊断需要将PFT结果与患者信息、症状和其他测试(如成像和血液分析)相结合。本研究旨在全面利用PFTs中的所有检测信息来辅助COPD的诊断。各种机器学习模型,如逻辑回归、支持向量机(SVM)、k近邻(KNN)、随机森林、决策树和XGBoost,已被用于建立COPD诊断辅助模型。XGBoost模型使用组LASSO算法提取的特征进行训练,获得了最佳性能,接收器工作特性曲线下面积(ROC)为0.90,准确率为88.6%,灵敏度为98.5%。该模型可以帮助医生进行COPD的临床诊断和早期预测。
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