Cross-sectional study shows frailty prediction in older adults using machine learning, indicating intervention strategies for aging populations.
ML algorithms can effectively predict the occurrence of frailty in Chinese elderly and identify significant factors associated with frailty. For datasets with complex feature interactions, LightGBM is preferable, while Random Forest offers improved stability and variance control. These findings support ML-based frailty screening and intervention strategies for aging populations.
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Yue Liu (2025) studied this question.
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