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Predicting Hepatitis B Virus Infection Based on Health Examination Data of Community Population

Author

Listed:
  • Ying Wang

    (Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China)

  • Zhicheng Du

    (Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China)

  • Wayne R. Lawrence

    (Department of Epidemiology and Biostatistics, School of Public Health, University at Albany, State University of New York, Rensselaer, New York, NY 12144, USA)

  • Yun Huang

    (Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China)

  • Yu Deng

    (Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China)

  • Yuantao Hao

    (Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China)

Abstract

Despite a decline in the prevalence of hepatitis B in China, the disease burden remains high. Large populations unaware of infection risk often fail to meet the ideal treatment window, resulting in poor prognosis. The purpose of this study was to develop and evaluate models identifying high-risk populations who should be tested for hepatitis B surface antigen. Data came from a large community-based health screening, including 97,173 individuals, with an average age of 54.94. A total of 33 indicators were collected as model predictors, including demographic characteristics, routine blood indicators, and liver function. Borderline-Synthetic minority oversampling technique (SMOTE) was conducted to preprocess the data and then four predictive models, namely, the extreme gradient boosting (XGBoost), random forest (RF), decision tree (DT), and logistic regression (LR) algorithms, were developed. The positive rate of hepatitis B surface antigen (HBsAg) was 8.27%. The area under the receiver operating characteristic curves for XGBoost, RF, DT, and LR models were 0.779, 0.752, 0.619, and 0.742, respectively. The Borderline-SMOTE XGBoost combined model outperformed the other models, which correctly predicted 13,637/19,435 cases (sensitivity 70.8%, specificity 70.1%), and the variable importance plot of XGBoost model indicated that age was of high importance. The prediction model can be used to accurately identify populations at high risk of hepatitis B infection that should adopt timely appropriate medical treatment measures.

Suggested Citation

  • Ying Wang & Zhicheng Du & Wayne R. Lawrence & Yun Huang & Yu Deng & Yuantao Hao, 2019. "Predicting Hepatitis B Virus Infection Based on Health Examination Data of Community Population," IJERPH, MDPI, vol. 16(23), pages 1-13, December.
  • Handle: RePEc:gam:jijerp:v:16:y:2019:i:23:p:4842-:d:293125
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    References listed on IDEAS

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    1. Stephen F Weng & Jenna Reps & Joe Kai & Jonathan M Garibaldi & Nadeem Qureshi, 2017. "Can machine-learning improve cardiovascular risk prediction using routine clinical data?," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-14, April.
    2. Pi Guo & Fangfang Zeng & Xiaomin Hu & Dingmei Zhang & Shuming Zhu & Yu Deng & Yuantao Hao, 2015. "Improved Variable Selection Algorithm Using a LASSO-Type Penalty, with an Application to Assessing Hepatitis B Infection Relevant Factors in Community Residents," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-23, July.
    3. Wai-Kay Seto & Chun-Fan Lee & Ching-Lung Lai & Philip P C Ip & Daniel Yee-Tak Fong & James Fung & Danny Ka-Ho Wong & Man-Fung Yuen, 2011. "A New Model Using Routinely Available Clinical Parameters to Predict Significant Liver Fibrosis in Chronic Hepatitis B," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-7, August.
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