MACHINE LEARNING APPROACHES FOR FALL PREDICTION IN KOREAN COMMUNITY-DWELLING OLDER ADULTS
2024
Machine Learning for Predicting Falls in Older Adults
Sample size: 9884
publication
Evidence: high
Author Information
Author(s): Cho Jungwon, Yang Minhee, Cho Eunhee
Primary Institution: Yonsei University
Hypothesis
Can machine learning models effectively predict falls among community-dwelling older adults?
Conclusion
Machine learning methods can accurately identify older adults at high risk for falls, enabling early interventions.
Supporting Evidence
- Falls are the second leading cause of death worldwide.
- Approximately 27% of older adults fall annually.
- The random forest model had the highest AUC value of 0.98.
Takeaway
This study used computers to help figure out which older people might fall, so they can get help before it happens.
Methodology
The study used various machine learning models on data from the 2020 Korean National Survey of Older Adults, including logistic regression and random forest.
Participant Demographics
Community-dwelling older adults in Korea.
Digital Object Identifier (DOI)
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