基于机器学习的老年人体质综合评价模型的构建  

Establishment of comprehensive evaluation models of physical fitness of the elderly based on machine learning

作　　者：刘小花 朱若灵 刘伟新[2] 田小利[3] 吴磊[2] LIU Xiao-Hua;ZHU Ruo-Ling;LIU Wei-Xin;TIAN Xiao-Li;WU Lei(The 2nd Affiliated Hospital of Nanchang University One-stop Service Center for Admission,Nanchang University,Nanchang 330006,China;School of Public Health,Jiangxi Medical College,Nanchang University,Jiangxi Provincial Key Laboratory of Preventive Medicine,Jiangxi Medical College,Nanchang University,Nanchang 330006,China;College of Life Sciences,Nanchang University,Nanchang 330038,China)

机构地区：[1]南昌大学第二附属医院入院一站式服务中心,南昌330006 [2]南昌大学公共卫生学院,江西省预防医学重点实验室,南昌330006 [3]南昌大学生命科学学院,南昌330038

出　　处：《生理学报》2023年第6期937-945,共9页Acta Physiologica Sinica

基　　金：supported by the Key Research and Development Project of Ministry of Science and Technology(No.2020YFC2002901);the National Natural Science Foundation of China(No.81560550,81960620,81360446)。

摘　　要：本研究旨在构建基于机器学习的老年人体质综合评价模型,为开展社区老年人体质监测提供重要依据。通过多阶段分层抽样,采用本研究团队前期研究成果《老年人体质综合测评量表》为体质调查问卷,对南昌社区≥60岁老年人进行体质测量,基于机器学习分别构建社区老年人体质综合评价的模糊神经网络(fuzzy neural network,FNN)、支持向量机(support vector machine,SVM)和随机森林(random forest,RF)模型。结果显示,FNN、SVM、RF构建的体质综合评价模型的准确度、灵敏度、特异度均分别在0.85、0.75、0.89以上,其中以FNN模型的预测性能最佳。以上结果提示,FNN、RF、SVM 3种机器学习模型在体质综合评价预测效果中均表现良好,可以作为开展老年人体质评价的工具。The present study aims to establish comprehensive evaluation models of physical fitness of the elderly based on machine learning,and provide an important basis to monitor the elderly’s physique.Through stratified sampling,the elderly aged 60 years and above were selected from 10 communities in Nanchang City.The physical fitness of the elderly was measured by the comprehensive physical assessment scale based on our previous study.Fuzzy neural network(FNN),support vector machine(SVM)and random forest(RF)models for comprehensive physical evaluation of the elderly people in communities were constructed respectively.The accuracy,sensitivity and specificity of the comprehensive physical fitness evaluation models constructed by FNN,SVM and RF were above 0.85,0.75 and 0.89,respectively,with the FNN model possessing the best prediction performance.FNN,RF and SVM models are valuable in the comprehensive evaluation and prediction of physical fitness,which can be used as tools to carry out physical evaluation of the elderly.

关 键 词：老年人 体质 机器学习 

分 类 号：R592[医药卫生—老年医学]