Prediction model of acute exacerbation of chronic obstructive pulmonary disease based on machine learning
10.3969/j.issn.1006-9771.2022.06.008
- VernacularTitle:基于机器学习的慢性阻塞性肺疾病急性加重预测模型的研究
- Author:
Bochao ZHANG
1
;
Zhao YANG
2
;
Liquan GUO
1
;
Jing CHEN
1
;
Daxi XIONG
1
Author Information
1. School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230026, China
2. Respiratory Department, the Affiliated Suzhou Science and Technology Town Hospital of Nanjing Medical University, Suzhou, Jiangsu 215163, China
- Publication Type:Journal Article
- Keywords:
chronic obstructive pulmonary disease;
acute exacerbation period;
machine learning;
prediction model
- From:
Chinese Journal of Rehabilitation Theory and Practice
2022;28(6):678-683
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveIn view of the problems of large errors and poor accuracy in pulmonary function testing in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD), a predictive classification model of pulmonary function in patients with AECOPD was proposed by comparing the prediction performance of different machine learning models to find the optimal model. MethodsFrom January, 2018 to February, 2020, 90 patients with different degrees of COPD from the Affiliated Suzhou Science and Technology Town Hospital of Nanjing Medical University were collected. Six machine learning model algorithms (K-nearest neighbor, logistic regression, support vector machine, naive Bayes, decision tree and random forest) were used to establish AECOPD predictive classification models. Their area under the curve of receiver operating characteristic (AUC-ROC) and accuracy were compared. Ten-fold cross-validation method was used to validate the data set. ResultsThe model based on random forest worked best in predicting and classifying AECOPD patients, with an accuracy rate of 0.844 and an AUC-ROC of 0.916. ConclusionRandom forest-based predictive model is a powerful tool for identifying patients with AECOPD, providing decision support when it is difficult to give a definitive diagnosis.
