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Machine Learning for Establishing the Precision Prediction of Sarcopenia
Chen-Cheng Yang1,2,3,4, Po-Hung Chen5, Cheng-Hong Yang5,6,7,8
1Department of Occupational and Environmental Medicine, Kaohsiung Municipal Siaogang Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.
Gerontology
|January 10, 2026
Summary
This study developed a machine learning model to predict sarcopenia, a condition linked to aging. The CatBoost model achieved 96.62% accuracy, identifying key predictors for early detection.
Area of Science:
- Gerontology and Computational Medicine
- Machine Learning in Healthcare
Background:
- Sarcopenia, a condition of muscle loss, poses significant health risks, especially in aging populations.
- Predictive models for sarcopenia are limited, hindering early diagnosis and intervention.
Purpose of the Study:
- To develop and evaluate machine learning models for sarcopenia prediction.
- Identify key predictors of sarcopenia using advanced analytical techniques.
Main Methods:
- Retrospective analysis of 1,441 participants' data, including demographics, lifestyle, and medical history.
- Evaluation of six machine learning models: CatBoost, KNN, NB, RF, GBDT, and XGBoost.
- Performance assessment using accuracy, precision, recall, and F1-Score; feature importance analysis via SHAP.
Main Results:
- CatBoost model demonstrated superior performance with 96.62% accuracy, high precision, recall, and F1-Score.
- Key predictors identified include age, gender, pulse rate, pulmonary disease, blood pressure, dizziness, and missing teeth.
- SHAP analysis provided insights into the influence of each feature on sarcopenia prediction.
Conclusions:
- The CatBoost model is a highly effective tool for predicting sarcopenia.
- Findings support the potential for early sarcopenia detection and intervention using machine learning.
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