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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    • References(20)
  • [1]
    胡盛寿, 高润霖, 刘力生, 等. 《中国心血管病报告2018》概要[J]. 中国循环杂志, 2019, 34(3): 209-220. doi: 10.3969/j.issn.1000-3614.2019.03.001
    [2]
    邹一帆, 徐滔, 赵婷, 等. 老年人内脏脂肪面积与血脂水平及冠心病的相关性研究[J]. 中华全科医学, 2020, 18(6): 909-912. https://www.cnki.com.cn/Article/CJFDTOTAL-SYQY202006008.htm
    [3]
    李富军, 杨利娟, 黄晓鸥, 等. 社区与家庭一体化管理模式在农村地区冠心病二级预防中的探索[J]. 中华全科医学, 2019, 17(8): 1360-1362. https://www.cnki.com.cn/Article/CJFDTOTAL-SYQY201908032.htm
    [4]
    熊日新, 林英忠. 人工智能在心血管疾病风险评估中的应用研究进展[J]. 中国临床新医学, 2020, 13(5): 537-540. doi: 10.3969/j.issn.1674-3806.2020.05.27
    [5]
    李婕, 向菲. 冠心病风险因素识别及其预测模型构建[J]. 中华医学图书情报杂志, 2020, 29(6): 7-13. doi: 10.3969/j.issn.1671-3982.2020.06.002
    [6]
    JAN G, OWACKI, MATEUSZ K, et al. Machine learning-based algorithm enables the exclusion of obstructive coronary artery disease in the patients who underwent coronary artery calcium scoring[J]. Academic Radiology, 2020, 27(10): 1416-1421. doi: 10.1016/j.acra.2019.11.016
    [7]
    施建伷, 蒋志新, 叶力, 等. 人工智能在冠心病诊断及危险度分层中的应用进展[J]. 医学研究生学报, 2019, 32(9): 973-977. https://www.cnki.com.cn/Article/CJFDTOTAL-JLYB201909016.htm
    [8]
    阿拉依·阿汗, 田翔华, 肖齐, 等. 关联规则与Logistic回归在维吾尔族健康体检人群代谢综合征数据挖掘中的应用[J]. 现代预防医学, 2018, 45(7): 1161-1165. https://www.cnki.com.cn/Article/CJFDTOTAL-XDYF201807003.htm
    [9]
    魏倩. 高维数据下改进Logistic回归模型及其应用研究[D]. 武汉: 中南财经政法大学, 2019.
    [10]
    谷鸿秋, 王春娟, 李子孝, 等. 基于Logistic回归与XGBoost构建缺血性卒中院内复发风险预测模型的初步比较研究[J]. 中国卒中杂志, 2020, 15(6): 587-594. doi: 10.3969/j.issn.1673-5765.2020.06.003
    [11]
    李惠萍, 胡安民. 机器学习DNN和XGBoost算法对危重患者预后预测模型效能评估[J]. 实用医学杂志, 2020, 36(4): 466-469. doi: 10.3969/j.issn.1006-5725.2020.04.009
    [12]
    中国心血管病预防指南(2017)写作组, 中华心血管病杂志编辑委员会. 《中国心血管病预防指南(2017)》冠心病二级预防要点[J]. 实用心脑肺血管病杂志, 2018, 26(1): 6. https://www.cnki.com.cn/Article/CJFDTOTAL-SYXL201801054.htm
    [13]
    尹春燕. 基于集成特征选择的冠心病筛查模型研究[D]. 济南: 山东大学, 2019.
    [14]
    LI D, XIONG G L, ZENG H S, et al. Machine learning-aided risk stratification system for the prediction of coronary artery disease[J]. Int J Cardiol, 2020, 326: 30-34. http://www.sciencedirect.com/science/article/pii/S0167527320339000
    [15]
    龚军, 杜超, 钟小钢, 等. 基于机器学习算法的原发性高血压并发冠心病的患病风险研究[J]. 解放军医学杂志, 2020, 45(7): 735-741. https://www.cnki.com.cn/Article/CJFDTOTAL-JFJY202007010.htm
    [16]
    魏珂, 司春婴, 王贺, 等. 人工智能在心血管疾病诊断及风险预测中的研究进展[J]. 世界科学技术-中医药现代化, 2020, 22(10): 3576-3582. https://www.cnki.com.cn/Article/CJFDTOTAL-SJKX202010024.htm
    [17]
    SUAT G, CEM B, SALIM Y, et al. PP-171 the role of cardiovascular risk factors and risk scoring systems in predicting coronary atherosclerosis[J]. AM J Cardiol, 2016, 117: S102. http://www.onacademic.com/detail/journal_1000039648878310_fe2c.html
    [18]
    HAN D, KOLLI K K, GRANSAR H, et al. Machine learning based risk prediction model for asymptomatic individuals who underwent coronary artery calcium score: Comparison with traditional risk prediction approaches[J]. J Cardiovasc Comput Tomogr, 2020, 14(2): 168-176. doi: 10.1016/j.jcct.2019.09.005
    [19]
    逄凯. 三种机器学习方法在冠心病筛查中的比较研究[D]. 长春: 吉林大学, 2016.
    [20]
    刘毅. 基于集成学习算法的冠心病早期筛查方法研究[D]. 济南: 山东大学, 2018.
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