Machine Learning-Assisted Diagnosis Model for Chronic Obstructive Pulmonary Disease

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.