[Establishment of comprehensive evaluation models of physical fitness of the elderly based on machine learning].
Sheng Li Xue Bao
; 75(6): 937-945, 2023 Dec 25.
Article
in Zh
| MEDLINE
| ID: mdl-38151355
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.
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Collection:
01-internacional
Database:
MEDLINE
Main subject:
Physical Fitness
/
Neural Networks, Computer
Limits:
Aged
/
Humans
Language:
Zh
Journal:
Sheng Li Hsueh Pao [Acta Physiologica Sinica]
/
Sheng Li Xue Bao
Year:
2023
Document type:
Article
Affiliation country:
China
Country of publication:
China