Application of neural network model and logistic regression in the prediction of chronic obstructive pulmonary disease
10.3969/j.issn.1006-2483.2021.02.003
- VernacularTitle:神经网络模型和logistic回归在预测慢性阻塞性肺疾病中的应用研究
- Author:
Yumeng TANG
1
;
Lan ZHANG
1
;
Qian LI
1
;
Jie HONG
2
;
Jianhua LI
3
;
Shuzhen ZHU
1
Author Information
1. Institute of Chronic and Non-communicable Disease Control and Prevention, Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China
2. Gong’an Center for Disease Control and Prevention, Jingzhou, Hubei 434300, China
3. Yingcheng Center for Disease Control and Prevention, Xiaogan 432400, China
- Publication Type:Journal Article
- Keywords:
Chronic obstructive pulmonary disease;
Artificial neural network;
Logistic regression;
Prediction
- From:
Journal of Public Health and Preventive Medicine
2021;32(2):12-16
- CountryChina
- Language:Chinese
-
Abstract:
Objective To establish a mathematical prediction model for chronic obstructive pulmonary disease (COPD) by applying an artificial neural network (ANN) and logistic regression analysis method. Methods A cross-sectional survey was conducted in 2015 to collect epidemiological data of COPD of 2 400 residents from Hubei Province. Subjects were randomized into training group and test group at a ratio of 7:3. The prediction models of COPD were established using ANN and logistic multiple regression. The predictive performance of the two models was compared. Results Information from a total of 1 569 subjects was valid and analyzed, including 1,099 cases in the training group and 470 cases in the test group. The area under curve (AUC) of ANN for training group and test group was 0.80 and 0.78, respectively. The AUC of logistic regression for training group and test group was 0.75 and 0.74, respectively. Conclusion It is feasible to apply ANN and logistic regression models to predict COPD, which can provide scientific evidence for COPD prevention and treatment.
