Establishment of comprehensive evaluation models of physical fitness of the elderly based on machine learning / 生理学报
Xiao-Hua LIU; Ruo-Ling ZHU; Wei-Xin LIU; Xiao-Li TIAN; Lei WU.
Acta Physiologica Sinica
; (6): 937-945, 2023.
Artículo
en Zh
| WPRIM | ID: wpr-1007802
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.
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