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

LIU Xiao-Hua¹, ZHU Ruo-Ling², LIU Wei-Xin^2,*, TIAN Xiao-Li³, WU Lei²

¹The 2nd Affiliated Hospital of Nanchang University One-stop Service Center for Admission, Nanchang University, Nanchang 330006, China；²School of Public Health, Jiangxi Medical College, Nanchang University; Jiangxi Provincial Key Laboratory of Preventive Medicine, Jiangxi Medical College, Nanchang University, Nanchang 330006, China；³College of Life Sciences, Nanchang University, Nanchang 330038, China

Abstract

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.

Key words: elderly; physical fitness; machine learning

Received: 　　Accepted:

Corresponding author: 刘伟新　　E-mail: liuweixinwendy@ncu.edu.cn

DOI: 10.13294/j.aps.2023.0084

Citing This Article：

LIU Xiao-Hua, ZHU Ruo-Ling, LIU Wei-Xin, TIAN Xiao-Li, WU Lei. Establishment of comprehensive evaluation models of physical fitness of the elderly based on machine learning. Acta Physiol Sin 2023; 75 (6): 937-945 (in Chinese with English abstract).