Comparison of the predictive performance of Logistic regression, BP neural network and support vector machine model for the risk of acute exacerbation of readmission in elderly patients with chronic obstructive pulmonary disease within 30 days

Abstract

To compare the effectiveness of Logistic regression, BP neural network and support vector machine models in the prediction of 30-day risk of readmission in elderly patients with an exacerbation of chronic obstructive pulmonary disease (COPD) and to provide a scientific basis for the screening and prevention of high-risk patients with readmission. The COPD patient survey questionnaire was made, including the general data questionnaire, modified Medical Research Council dyspnea scale (mMRC), activities of daily living (ADL), the geriatric depression scale, the mini nutritional assessment-short form (MNA-SF) and COPD assessment test (CAT). Elderly COPD patients were selected from the department of respiratory medicine of 13 general hospitals in Ningxia from April 2019 to August 2020 by convenience sampling method, and they were followed up 30 days after discharge. To explore the risk factors of patient readmission, Logistic regression model, BP neural network model and support vector machine models were constructed based on the risk factors. According to the ratio of the training set to the testing set of 7:3, the model was divided into the training set sample and the testing set sample. The prediction efficiency of the model was compared by the precision rate, recall rate and accuracy rate, F1 index and the area under the receiver operator characteristic curve (AUC). A total of 1 120 patients were investigated, including 879 non-readmission patients and 241 readmission patients. Univariate regression analysis showed that there were statistically significant differences in age, education level, smoking status, proportion of diabetes and coronary heart disease, hospitalization times of acute exacerbation of COPD in the past 1 year, seasonal factors and long-term home oxygen therapy, regular medication, proportion of rehabilitation exercise, course of disease, ADL, depression status, mMRC, nutritional status between non-readmission patients and readmission patients. Binary Logistic regression analysis showed that education level, smoking status, coronary heart disease, hospitalization times of acute exacerbation of COPD in the past 1 year, seasonal factors, whether long-term home oxygen therapy, whether regular medication, nutritional status were the risk factors for 30-day acute exacerbation of readmission in elderly patients with COPD. The training set showed that the accuracy rate of Logistic regression model, BP neural network model and support vector machine models were 70.95%, 76.51% and 84.78%, respectively. The recall rates were 79.55%, 86.36% and 88.64%, respectively. The accuracy rates were 87.81%, 90.81% and 93.82%, respectively. F1 indexes were 0.75, 0.81 and 0.87, respectively. The AUC were 0.850, 0.893 and 0.921, respectively. The testing set showed that the precision rate of Logistic regression model, BP neural network model and support vector machine model were 78.38%, 80.65% and 88.57%, respectively. The recall rates were 70.73%, 60.98% and 75.61%, respectively. The accuracy rates were 85.82%, 84.40% and 90.07%, respectively. F1 indexes were 0.74, 0.69 and 0.82, respectively. The AUC were 0.814, 0.775 and 0.858, respectively. Comparing with Logistic regression and BP neural network, support vector machine model has better prediction effect, and can effectively predict the risk of acute exacerbation of readmission in elderly patients with COPD within 30 days.