Abstract
Objective: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.
Export citation and abstract BibTeX RIS
During the embargo period (the 12 month period from the publication of the Version of Record of this article), the Accepted Manuscript is fully protected by copyright and cannot be reused or reposted elsewhere.
As the Version of Record of this article is going to be / has been published on a subscription basis, this Accepted Manuscript will be available for reuse under a CC BY-NC-ND 3.0 licence after the 12 month embargo period.
After the embargo period, everyone is permitted to use copy and redistribute this article for non-commercial purposes only, provided that they adhere to all the terms of the licence https://creativecommons.org/licences/by-nc-nd/3.0
Although reasonable endeavours have been taken to obtain all necessary permissions from third parties to include their copyrighted content within this article, their full citation and copyright line may not be present in this Accepted Manuscript version. Before using any content from this article, please refer to the Version of Record on IOPscience once published for full citation and copyright details, as permissions may be required. All third party content is fully copyright protected, unless specifically stated otherwise in the figure caption in the Version of Record.