Diagnosing chronic obstructive pulmonary disease (COPD) using Impulse Oscillometry (IOS) is challenging due to the high level of clinical expertise it demands from doctors , which limits the clinical application of IOS in screening. The primary aim of this study is to develop a COPD diagnostic model based on machine learning algorithms using IOS test results. 
Approach:Feature selection was conducted to identify the optimal subset of features from the original feature set, which significantly enhanced the classifier's performance. Additionally, secondary features area of reactance (AX) were derived from the original features based on clinical theory, further enhancing the performance of the classifier. The performance of the model was analyzed and validated using various classifiers and hyperparameter settings to identify the optimal classifier. We collected 528 clinical data examples from the China-Japan Friendship Hospital for training and validating the model. 
Main results:The proposed model achieved reasonably accurate diagnostic results in the clinical data (accuracy=0.920, specificity=0.941, precision=0.875, recall=0.875). 
Significance:The results of this study demonstrate that the proposed classifier model, feature selection method, and derived secondary feature AX provide significant auxiliary support in reducing the requirement for clinical experience in COPD diagnosis using IOS.
.
Keywords: COPD; K-nearest neighbors; feature derivation; machine learning; respiratory system mechanics.
© 2024 Institute of Physics and Engineering in Medicine.