Prediction model of COPD acute exacerbation with big data by machine learning methods

Introduction: There has been little study regarding prediction of acute exacerbation. Aims and Objective: This study aimed to develop prediction model of COPD acute exacerbation with big data by machine learning methods. Methods: We investigated data from 594 COPD patients who were enrolled in the KOCOSS cohort. Smoking status, lung function, body mass index, and COPD assessment test (CAT) score were collected from cohort data. We merged patients’ information with the Korean Health Insurance database. Comorbidity, health care utilization, moderate to severe exacerbation, and COPD medications between 2008 and 2012 were collected. We also collected daily air pollution level, temperature, humidity, and wind velocity. Data on the activities of respiratory viruses were collected. Prediction model was developed by deep learning methods and also statistical method. Deep neural network (DNN), random forest, and generalized estimating equation (GEE) were utilized. Results: Area under the curve (AUC) value for prediction of acute exacerbation was highest by random forest method (0.8722) followed by GEE (0.8617) and DNN (0.8493). Sensitivity was highest by random forest (0.8613) followed by DNN (0.8000) and GEE (0.7613). In GEE analysis, female, CAT score, FEV1 (%), number of exacerbations in previous one year, use of bronchodilator, history of asthma, influenza, and human coronavirus were significantly associated with acute exacerbation. Conclusions: Prediction model for acute exacerbation of COPD was developed with big data by machine learning methods. AUC and sensitivity were higher in model by machine learning methods compared with GEE.