Identify and Characterize Fall-Risk in Older Adults: A Data-Driven Approach
- Resource Type
- Conference
- Authors
- Fu, Enqi; Tang, Huimin; Xie, Xiaolei; Kang, Lin
- Source
- 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Systems, Man, and Cybernetics (SMC), 2023 IEEE International Conference on. :4122-4127 Oct, 2023
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Sociology
Predictive models
Boosting
Behavioral sciences
Older adults
Statistics
Cybernetics
geriatric management
fall-risk prediction
clustering
- Language
- ISSN
- 2577-1655
In this study we introduce a precise fall-risk screening method for large-scale older population. Based on a dataset including 7084 older adults across 30 provinces in China, we developed a data-driven method to identify the fall-risk group and determine the major characteristics in older adults. First, the entire sample were divided into two groups by gender based on analysis of Cluster Feature Tree. Extreme Gradient Boosting models confirmed that patient clustering can improve the performance of fall-risk prediction, and pinpointed the common and different important features for different patient groups. The findings provide evidence for future behavioral trait indicators for geriatric rehabilitation and have potential to enhance geriatric health management in primary care.