A Model Using Support Vector Machines Recursive Feature Elimination (SVM-RFE) Algorithm to Classify Whether COPD Patients Have Been Continuously Managed According to GOLD Guidelines

• Published in: International journal of chronic obstructive pulmonary disease Vol. 15; pp. 2779 - 2786
• Main Authors: Xia, Jie, Sun, Lina, Xu, Suqin, Xiang, Qiu, Zhao, Jianping, Xiong, Weining, Xu, Yongjian, Chu, Shuyuan
• Format: Journal Article
• Language: English
• Published: London Dove Medical Press Limited 01.01.2020; Dove Medical Press Ltd; Dove; Dove Medical Press
• Subjects: Algorithms; Analysis; Chronic obstructive pulmonary disease; Classification; continuous management; copd; Datasets; gold; Hospitals; Lung diseases, Obstructive; Medical care; Medical prognosis; Original Research; Patients; Prognosis; Quality management; support vector machine; Support vector machines; China; United States > US
• ISSN: 1178-2005; 1176-9106; 1178-2005
• DOI: 10.2147/COPD.S271237

Purpose: Patients with chronic obstructive pulmonary disease (COPD) would have a poor prognosis if they were not continuously managed according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines. We aim to develop a model to classify whether COPD patients have been continuously managed according to GOLD in the previous year. Methods: The Managed group were COPD patients from a prospective cohort from November 2017 to November 2019, who have been continuously managed according to GOLD for 1 year. The Control group were COPD patients who were not continuously managed according to GOLD. They were from a retrospective cohort from October 2016 to October 2017 in the same hospitals as the Managed group. A synthetic minority oversampling technique (SMOTE) algorithm was used to up-sample the Managed group in a training dataset. Features for classification were selected using a support vector machine recursive feature elimination (SVM-RFE) algorithm. The classification model was developed using LibSVM, and its performance was assessed on the testing dataset. Results: The final analysis included 15 subjects in the Managed group and 191 in the Control group. SVM-RFE selects nine features including smoking history, post-bronchodilator (post-)FVC before management, and those after 1-year follow-up (BMI, moderate and severe AECOPD frequency in previous 12 months, mMRC score, post-FEV1, post-FEV1%pred, post-FVC, and post-FEV1/FVC). For our model, positive predictive value is 66.7%, F1 score is 0.978, and AUC is 0.987. Conclusion: SVM classifier combined with SVM-REF feature selection algorithm could achieve good classification between COPD patients who are or are not continuously managed. This model could be applied in clinical practice to help doctors make decisions and enhance COPD patients' compliance with standard treatment. Keywords: COPD, GOLD, continuous management, support vector machine