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Machine-learning classifier models for predicting sarcopenia in the elderly based on physical factors.

¹Department of Physical Therapy, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, South Korea.

Geriatrics & Gerontology International

|May 14, 2024

Summary

This study developed a machine learning model to predict sarcopenia in older adults using physical and activity data. The LightGBM model achieved high accuracy, offering a non-imaging tool for early sarcopenia detection.

Keywords:

machine learning physical activity physical characteristics predictive model sarcopenia

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Area of Science:

Gerontology
Biomedical Informatics
Musculoskeletal Health

Background:

The aging global population is experiencing a rise in musculoskeletal disorders, particularly sarcopenia.
Current diagnostic methods for sarcopenia, including imaging techniques, can be resource-intensive.
Machine learning approaches are emerging as promising tools for sarcopenia prediction.

Purpose of the Study:

To develop a predictive model for sarcopenia in individuals aged 60 and older.
To utilize physical characteristics and activity-related variables for prediction, avoiding the need for medical imaging equipment.
To assess the performance of various machine learning algorithms in sarcopenia prediction.

Main Methods:

Public data from the Korea National Health and Nutrition Examination Survey was used.
Sarcopenia prediction models were constructed using Logistic Regression, Support Vector Machine (SVM), XGBoost, LightGBM, RandomForest, and Multi-layer Perceptron Neural Network (MLP) algorithms.
Feature importance was analyzed for models, excluding SVM and MLP.

Main Results:

The LightGBM algorithm yielded the highest test accuracy, achieving 0.848.
Key predictive variables included physical characteristics like body mass index, weight, and waist circumference.
Activity-related variables also contributed significantly to the model's predictive power.

Conclusions:

A sarcopenia prediction model based solely on physical and activity factors demonstrated high performance.
This model shows potential for early sarcopenia detection in elderly populations, especially in resource-limited settings.
The findings support the use of accessible data for sarcopenia screening.