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Sarcopenia Prediction for Elderly People Using Machine Learning: A Case Study on Physical Activity.

Minje Seok1, Wooseong Kim1

  • 1Computer Engineering Department, Gachon University, Seongnam 13120, Gyeonggi, Republic of Korea.

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|May 13, 2023
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Summary
This summary is machine-generated.

Predicting age-related sarcopenia is improved by machine learning models using physical activity (PA) and obesity data. Deep neural networks achieved up to 90% accuracy, highlighting PA and waist circumference as key predictors.

Keywords:
KNHANESelderlymachine learningphysical activitysarcopenia

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Area of Science:

  • Gerontology
  • Biomedical Engineering
  • Machine Learning

Background:

  • Sarcopenia, an age-related muscle disorder, is linked to musculoskeletal issues and metabolic syndromes like sarcopenic obesity.
  • Predictive models for sarcopenia often rely on factors requiring medical equipment.
  • Physical activity (PA) is a measurable risk factor, accessible via personal devices and lifelogs.

Purpose of the Study:

  • To explore the impact of daily physical activity and obesity on sarcopenia prediction.
  • To demonstrate the feasibility of using machine learning for sarcopenia prediction with accessible data.
  • To compare the performance of various machine learning models in predicting sarcopenia.

Main Methods:

  • Utilized data from the Korea National Health and Nutrition Examination Survey.
  • Trained multiple machine learning models: DNN, GBM, XGB, LGB, CAT, Logistic Regression, SVC, KNN, RF.
  • Evaluated models using PA features, obesity metrics (total fat mass, fat percentage, waist circumference), and SHAP analysis for feature importance.

Main Results:

  • Deep Neural Network (DNN) achieved 81% accuracy with PA features alone.
  • Accuracy increased to 90% with the addition of obesity information (fat mass, fat percentage).
  • DNN achieved 84% accuracy using PA and waist circumference, outperforming other models.
  • Several models (GBM, XGB, LGB, CAT, RF, DNN) showed significant predictive performance (AUC ~0.85-0.9) with PA and waist circumference.
  • Key predictors identified: quantified PA, metabolic equivalent score, BMI, and waist circumference.

Conclusions:

  • Machine learning models, particularly DNNs, can effectively predict sarcopenia using accessible physical activity and obesity data.
  • Waist circumference serves as a practical and accurate obesity metric for sarcopenia prediction when combined with PA.
  • Quantified PA and simple obesity measures are crucial features for developing accurate sarcopenia predictive models.