Volume 20 Issue 12
Dec.  2022
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Article Navigation >  Chinese Journal of General Practice  > 2022  >  20(12): 2080-2083
LI Li-qiu, XU Cheng-yan, WANG Xiao-li, CAO Yong-qi, LI Yan, ZHAO Liang, WANG Zhao-xin, JIA Huan. XGboost prediction model for osteoarthritis risk based on community big data[J]. Chinese Journal of General Practice, 2022, 20(12): 2080-2083. doi: 10.16766/j.cnki.issn.1674-4152.002774
Citation: LI Li-qiu, XU Cheng-yan, WANG Xiao-li, CAO Yong-qi, LI Yan, ZHAO Liang, WANG Zhao-xin, JIA Huan. XGboost prediction model for osteoarthritis risk based on community big data[J]. Chinese Journal of General Practice, 2022, 20(12): 2080-2083. doi: 10.16766/j.cnki.issn.1674-4152.002774
shu

XGboost prediction model for osteoarthritis risk based on community big data

doi: 10.16766/j.cnki.issn.1674-4152.002774
  • 1.

    Zhuanqiao Community Health Service Center, Minhang District, Shanghai 201108, China

Funds:

 71774116

 PW2019A-42

 CHDI-2021-B-08

 沪卫人事2020087号

 2020MHZ082

  • Received Date: 2022-03-18
    Available Online: 2023-02-07
    Abstract
  •   Objective  To explore the construction of osteoarthritis risk warning model by community medical big data and machine learning model, provide a quantitative tool for the early warning of osteoarthritis in the community, to provide an efficient management method for the prevention and treatment of osteoarthritis in the elderly.  Methods  The data of health records, health examinations and diagnosis and treatment data of six community health service centres in Shanghai from January 1, 2019 to December 31, 2019, were integrated to form an original database containing more than 40 000 samples and 126 variables. After data pre-processing and compound feature selection to screen the model characteristics, the XGBoost algorithm was used to construct a risk assessment model for osteoarthritis patients.  Results  Fourteen characteristics were screened in this study: diet with balanced meat and vegetables, height, weight, body mass index (BMI), time of each exercise, total cholesterol, high-density lipoprotein, low-density lipoprotein, hypertension, limb trauma, etc. High-density lipoprotein, total cholesterol, BMI, low-density lipoprotein and frequency of drinking were the top five characteristic factors in importance ranking, and their characteristic importance was more than 0.1. The XGBoost model of osteoarthritis risk assessment was constructed with 'osteoarthritis' as the output variable, and 14 features were screened by feature engineering as the input variable. After the XGBoost model was trained by eightfold cross-validation, the model was validated on the test set with an accuracy rate of 92%, a precision rate of 71% and recall rate of 65%, F1_score was 0.68, the area under the receiver operating characteristic curve reached 0.82, and the KS value was 0.48.  Conclusion  In this study, a risk warning model of osteoarthritis is constructed using community medical big data, and the overall fit and feature rationality of the model are good, which provides a tool for the early warning of osteoarthritis in the community and is conducive to the early diagnosis and treatment of osteoarthritis in the community.

     

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