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Applying machine learning methods to develop a successful aging maintenance prediction model based on physical fitness tests
Author(s) -
Cai TianPan,
Long JingWen,
Kuang Jie,
You Fu,
Zou TingTing,
Wu Lei
Publication year - 2020
Publication title -
geriatrics and gerontology international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.823
H-Index - 57
eISSN - 1447-0594
pISSN - 1444-1586
DOI - 10.1111/ggi.13926
Subject(s) - logistic regression , random forest , machine learning , medicine , decision tree , artificial intelligence , predictive modelling , gradient boosting , physical fitness , support vector machine , physical therapy , computer science
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; ••: ••–•• .