Using Machine Learning Algorithms to Predict Hepatitis B Surface Antigen Seroclearance
Joint Authors
Guo, Pi
Li, Xiangyong
Tian, Xiaolu
Chong, Yutian
Huang, Yutao
Li, Mengjie
Zhang, Wangjian
Du, Zhicheng
Hao, Yuantao
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-06-11
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
Hepatitis B surface antigen (HBsAg) seroclearance during treatment is associated with a better prognosis among patients with chronic hepatitis B (CHB).
Significant gaps remain in our understanding on how to predict HBsAg seroclearance accurately and efficiently based on obtainable clinical information.
This study aimed to identify the optimal model to predict HBsAg seroclearance.
We obtained the laboratory and demographic information for 2,235 patients with CHB from the South China Hepatitis Monitoring and Administration (SCHEMA) cohort.
HBsAg seroclearance occurred in 106 patients in total.
We developed models based on four algorithms, including the extreme gradient boosting (XGBoost), random forest (RF), decision tree (DCT), and logistic regression (LR).
The optimal model was identified by the area under the receiver operating characteristic curve (AUC).
The AUCs for XGBoost, RF, DCT, and LR models were 0.891, 0.829, 0.619, and 0.680, respectively, with XGBoost showing the best predictive performance.
The variable importance plot of the XGBoost model indicated that the level of HBsAg was of high importance followed by age and the level of hepatitis B virus (HBV) DNA.
Machine learning algorithms, especially XGBoost, have appropriate performance in predicting HBsAg seroclearance.
The results showed the potential of machine learning algorithms for predicting HBsAg seroclearance utilizing obtainable clinical data.
American Psychological Association (APA)
Tian, Xiaolu& Chong, Yutian& Huang, Yutao& Guo, Pi& Li, Mengjie& Zhang, Wangjian…[et al.]. 2019. Using Machine Learning Algorithms to Predict Hepatitis B Surface Antigen Seroclearance. Computational and Mathematical Methods in Medicine،Vol. 2019, no. 2019, pp.1-7.
https://search.emarefa.net/detail/BIM-1130662
Modern Language Association (MLA)
Tian, Xiaolu…[et al.]. Using Machine Learning Algorithms to Predict Hepatitis B Surface Antigen Seroclearance. Computational and Mathematical Methods in Medicine No. 2019 (2019), pp.1-7.
https://search.emarefa.net/detail/BIM-1130662
American Medical Association (AMA)
Tian, Xiaolu& Chong, Yutian& Huang, Yutao& Guo, Pi& Li, Mengjie& Zhang, Wangjian…[et al.]. Using Machine Learning Algorithms to Predict Hepatitis B Surface Antigen Seroclearance. Computational and Mathematical Methods in Medicine. 2019. Vol. 2019, no. 2019, pp.1-7.
https://search.emarefa.net/detail/BIM-1130662
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130662