Using Machine Learning to Build an Early Warning Model for the Risk of Severe Airflow Limitation in Patients with Chronic Obstructive Pulmonary Disease

BackgroundThe degree of airflow limitation is a key indicator of the progression degree in COPD patients. However, problems such as contraindications to testing and compliance make it difficult for some patients to undergo the relevant tests and evaluate the severity of the disease.ObjectiveTo develop and evaluate a machine learning algorithm-based early warning model for the risk of severe airflow limitation in COPD patients.MethodsA cross-sectional design was used to investigate COPD inpatients in a tertiary hospital in Sichuan Province from 2019-01 to 2020-06. General clinical indexes and pulmonary function test data were collected. The data were randomly divided into training and test sets in the ratio of 8∶2, and 216 risk warning models were constructed in the training set using four missing value filling methods, three feature screening methods, 17 machine learning and one integrated learning algorithm. The area under the ROC curve (AUC) , accuracy, precision, recall and F1 score were used to evaluate t