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The Identification of Elderly People with High Fall Risk Using Machine Learning Algorithms.

Ziyang Lyu1, Li Wang2, Xing Gao1

  • 1Institute of Smart Ageing, Beijing Academy of Science and Technology, Beijing 100035, China.

Healthcare (Basel, Switzerland)
|January 8, 2023
PubMed
Summary

Predicting fall risk in seniors is crucial for injury prevention. This study uses multifractal analysis and machine learning to cost-effectively identify high-risk elderly individuals, with Gradient Boosting Decision Trees showing the best performance.

Keywords:
elderlyfall riskmachine learningmultifractal algorithm

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Area of Science:

  • Gerontology
  • Public Health
  • Biomedical Engineering

Background:

  • Falls represent a significant public health concern among the elderly, leading to injuries and increased healthcare costs.
  • Current fall risk assessment methods can be resource-intensive for healthcare professionals and community workers.
  • Developing cost-effective and accurate methods for identifying high-risk elderly individuals is essential for targeted interventions.

Purpose of the Study:

  • To evaluate the efficacy of multifractal analysis combined with machine learning algorithms for identifying elderly individuals at high risk of falling.
  • To establish a cost-effective approach for fall risk prediction in the elderly population.
  • To compare the performance of different machine learning classifiers using motion trajectory stability as a key feature.

Main Methods:

  • A 42-point human body calibration model was developed to record three-dimensional coordinate datasets.
  • Multifractal analysis was employed to calculate the stability of motion trajectories, serving as an input feature.
  • Six distinct machine learning classifiers were trained and compared using the derived motion stability data.

Main Results:

  • A significant difference in motion instability was observed between the faller and no-faller groups across both male and female cohorts (p < 0.005).
  • The Gradient Boosting Decision Tree classifier demonstrated superior performance in distinguishing between individuals with high and low fall risk.
  • Multifractal features derived from motion trajectory stability proved effective in fall risk prediction.

Conclusions:

  • Multifractal analysis and machine learning offer a promising, cost-effective method for identifying elderly individuals at high fall risk.
  • The findings support the use of motion trajectory instability as a sensitive indicator for fall risk assessment.
  • This approach can facilitate personalized risk factor identification and the development of tailored fall prevention strategies for the elderly.