Applying machine learning methods to develop a successful aging maintenance prediction model based on physical fitness tests
Applying machine learning methods to develop a successful aging maintenance prediction model based on physical fitness tests
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应用机器学习方法开发基于体能测试的成功老化维持预测模型
DOI：
10.1111/ggi.13926
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发表时间：
2020-05-01
影响因子：
3.3
通讯作者：
Wu Lei
中科院分区：
文献类型：
--
作者：
Cai TianPan;Long JingWen;Wu Lei
目的建立基于体质测试的成功衰老（SA）机器学习预测模型。方法选取南昌市社区居民3657例，年龄为60岁。对所有参与者进行了为期3年的随访测试，以确定他们是否转向非sa。采用问卷调查和体能测试来获得整体健康状况、平衡、敏捷性、速度、反应和步态。采用逻辑回归、深度学习、随机森林和梯度增强决策树四种机器学习模型建立预测模型，分析样本为890个。结果基线成功衰老率为26.99%，SA对非SA年平均发病率为11.04%。在基线的所有体能测试中，SA组和非SA组之间存在显著差异。四种机器学习模型的准确率和曲线下面积平均为b> 85%，阳性预测值和灵敏度平均为b> 75%，特异性平均为b> 86%。深度学习模型的曲线下面积为90.00%，准确率为89.3%，阳性预测值为85.8%，特异性为93.1%，优于其他模型。与其他模型相比，逻辑回归模型的灵敏度最好。年龄、臂屈度、30秒坐立和反应时间是所有模型的重要预测因子。结论深度学习模型对SA维持的预测效果较好，相应的体质干预是保证SA维持的关键。Geriatr Gerontol Int 2020；中心网点中心网点：中心网点中心网点中心网点中心网点中心网点。
Aim The purpose of this study was to develop a machine learning prediction model for successful aging (SA) based on physical fitness tests.Methods A total of 3657 community-dwelling adults aged >= 60 years from Nanchang city were recruited in this study. A 3-year follow-up test was carried out for all the participants to determine whether they turn to non-SA. Developed questionnaires and physical fitness tests were used to obtain overall health condition, balance, agility, speed, reactions and gait. Four machine learning models (logistic regression, deep learning, random forest and gradient boosting decision tree) were applied to develop the prediction models, the analyzed sample was 890.Results The baseline prevalence of successful aging was 26.99%, The average annual incidence rate of SA to non-SA was 11.04%. There were significant differences between the SA and non-SA groups for all physical fitness tests at baseline. The accuracy and area under the curve of all four machine learning models was >85%, the positive predictive value and sensitivity was >75%, and the specificity was >86% on the average. The deep learning model outperformed the other model, with area under the curve 90.00%, accuracy 89.3%, positive predictive value 85.8% and specificity 93.1%, respectively. Compared with other models, the logistic regression model performed best in sensitivity. Age, arm curl, 30-s sit-to-stand and reaction time were important predictors in all models.Conclusion The deep learning model is ideal in the prediction of SA maintenance, and the corresponding physical fitness interventions are essential to ensuring SA. Geriatr Gerontol Int 2020; center dot center dot: center dot center dot-center dot center dot.