Volume 19 Issue 4
Apr.  2021
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Article Navigation >  Chinese Journal of General Practice  > 2021  >  19(4): 523-527
WANG Xiao-li, SHI Tian-xing, PENC De-rong, WANG ZIhao-xin, WANC Hui, SHI Jian-wei, YU Wen-ya. Comparative study on the effectiveness of two machine learning algorithms in constructing risk assessment models of coronary heart disease in the elderly[J]. Chinese Journal of General Practice, 2021, 19(4): 523-527. doi: 10.16766/j.cnki.issn.1674-4152.001852
Citation: WANG Xiao-li, SHI Tian-xing, PENC De-rong, WANG ZIhao-xin, WANC Hui, SHI Jian-wei, YU Wen-ya. Comparative study on the effectiveness of two machine learning algorithms in constructing risk assessment models of coronary heart disease in the elderly[J]. Chinese Journal of General Practice, 2021, 19(4): 523-527. doi: 10.16766/j.cnki.issn.1674-4152.001852
shu

Comparative study on the effectiveness of two machine learning algorithms in constructing risk assessment models of coronary heart disease in the elderly

doi: 10.16766/j.cnki.issn.1674-4152.001852
  • 1.

    Pudong Institute for Health Development, Shanghai 201208, China

Funds:

 71774116

 2018YFC2000700

 PW2019A-42

 2019PJC072

 2018YQ52

 201940052

  • Received Date: 2020-10-30
    Available Online: 2022-02-16
    Abstract
  •   Objective  The aim of this study is to established the ta risk assessment models for coronary heart disease in elderly based on machine learning algorithms and provide a more efficient health management methods for the prevention of coronary heart disease in the elderly. and compared the effectiveness of Logical regression and XGBoost for the risk prediction of coronary heart disease in elderly.  Methods  Data records of 47 community health service centers in Pudong area from January to December in 2019 were extracted from the regional health information platform of Shanghai Pudong health development research institute. Using Python Panda, 80 000 physical examination data of the elderly were included to build the model. Twenty-seven variables were selected by feature engineering to build the model, and logistic and xgboost were used to construct the model respectively.  Results  The optimal parameter of XGBoost model: learning_rate=0.1, Tree depth=8, Minimum node weight=5, Number of cycles=50. The optimal parameters of logistic model: C=1, class_weight=None, max_iter=100, solver=newton-cg. The accuracies of XGBoost and logistic were 0.82 and 0.71, and the area under the receiver operating characteristic curve was 0.85 and 0.80. The importance of XGBoost model is concentrated in a few features, and the importance of the first nine features accounts for 94.2% of the relative importance, while the importance of logistic model is relatively balanced among the features, and the importance of the first nine features accounts for 59.5% of the relative importance.  Conclusion  The coronary heart disease risk assessment model based on the physical examination data of the elderly in the community has good stability, and the efficiency of the model constructed by XGBoost is better than that of the logistic regression, which can provide a method for coronary heart disease risk assessment of the elderly in the community.

     

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