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Machine Learning for Sarcopenia Prediction in the Elderly Using Socioeconomic, Infrastructure, and Quality-of-Life

Minje Seok¹, Wooseong Kim¹, Jiyoun Kim²

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

Healthcare (Basel, Switzerland)

|November 14, 2023

Summary

Machine learning models accurately predict sarcopenia, a critical health concern for aging populations. Key factors include socioeconomic status, social infrastructure, and quality of life, enabling better management strategies.

Keywords:

explainable AI infrastructure machine learning quality of life sarcopenia socioeconomic

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Area of Science:

Gerontology
Public Health
Biomedical Informatics

Background:

The World Health Organization's "healthy aging" focus highlights the importance of functional ability in the elderly.
Sarcopenia, the loss of skeletal muscle mass, is a growing concern, particularly in rapidly aging societies like South Korea.
Understanding the interplay of socioeconomic status (SES), social infrastructure, and quality of life (QoL) is crucial for addressing sarcopenia prevalence.

Purpose of the Study:

To develop and validate machine learning (ML) models for predicting sarcopenia using national health survey data.
To investigate the influence of SES, social infrastructure, and QoL on sarcopenia prevalence in the South Korean population.
To identify key predictive features for sarcopenia using explainable AI techniques.

Main Methods:

Utilized machine learning algorithms including Random Forest (RF), LightGBM (LGB), CatBoost (CAT), and Deep Neural Networks (DNN).
Employed data from the Korea National Health and Nutrition Examination Survey (KNHANES) from 2008-2011.
Applied SHapley Additive exPlanations (SHAP) for feature importance and causal relationship analysis.

Main Results:

Achieved approximately 80% accuracy in sarcopenia identification across multiple ML models.
Demonstrated strong predictive performance with Area Under Curve (AUC) values of 0.831 (both genders), 0.868 (males), and 0.773 (females).
Identified monthly household income, housing type, marriage status, social infrastructure accessibility, neighborhood sports facility area, and life satisfaction as significant predictors.

Conclusions:

Machine learning models effectively predict sarcopenia, offering a valuable tool for public health initiatives.
Socioeconomic factors and social environment significantly influence sarcopenia risk, underscoring the need for integrated management strategies.
Explainable AI (SHAP) successfully elucidated key drivers of sarcopenia, facilitating targeted interventions for healthy aging.