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Related Experiment Video

Updated: Sep 18, 2025

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Development of Fall Risk Classification Models for Community-Dwelling Older Adults using Latent Class Analysis and

Suyeong Bae¹, Mi Jung Lee², Daewoo Pak³

¹Department of Occupational Therapy, Graduate School, Yonsei University, Wonju-si, Republic of Korea, sbae1@yonsei.ac.kr.

|June 24, 2025

Summary

This summary is machine-generated.

This study identified three distinct fall-risk groups among older adults in South Korea using latent class analysis. Key factors influencing fall risk include self-rated health and cognitive function, enabling tailored prevention strategies.

Keywords:

Accident prevention Accidental falls Aged Classification model Machine learning

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Last Updated: Sep 18, 2025

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Area of Science:

Gerontology
Public Health
Data Science

Background:

Community-dwelling older adults face significant fall risks.
Accurate identification of fall-risk groups is crucial for effective prevention.

Purpose of the Study:

To identify distinct fall-risk groups among community-dwelling older adults in South Korea.
To develop a classification model for investigating factors associated with fall risk.

Main Methods:

Utilized data from 9,231 older adults from the 2020 Korea Elderly Survey.
Employed latent class analysis to define fall-risk groups based on fall indicators.
Developed classification models (XGBoost) to predict group membership.

Main Results:

A three-class model (low, moderate, high fall risk) demonstrated optimal interpretability and fit.
The XGBoost model achieved high performance (accuracy=0.70, F1-score=0.68).
Significant risk factors included self-rated health, cognitive function, healthcare utilization, and need for assistance with daily living.

Conclusions:

The study successfully differentiated fall-risk levels, supporting a preventive healthcare approach.
Identified key risk factors provide a foundation for developing personalized fall prevention programs for older adults.