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Objective <p>To construct a risk prediction model for sarcopenic obesity in the elderly using different machine learning methods.</p> Methods <p>The research data were derived from the CHARLS 2015 national survey data. According to the inclusion and exclusion criteria, 2,375 elderly people were selected and 42 research variables were included. The risk factors were screened by univariate analysis. The variables with statistically significant differences (P &lt; 0.05) were selected using the Boruta feature selection method, resulting in 16 features. A prediction model was then constructed based on six machine learning algorithms. The model was comprehensively evaluated using the area under the receiver operating characteristic curve (AUROC), accuracy, recall, precision, F1 score, and Brier score. The interpretable analysis of the optimal machine learning model was performed with SHAP（Shapley Additive Explanations. </p> Results <p>Among the six models, XGBoost had the best comprehensive performance, with an AUC of 0.78, accuracy of 0.89, recall of 0.20, precision of 0.53, F1 score of 0.28, and Brier score of 0.11. The importance analysis of shap features showed that waist circumference, pace and uric acid were important risk factors. </p> Conclusion <p>The model constructed by XGBoost machine learning algorithm has the best predictive performance, which may facilitate for early clinical evaluation and prevention of sarcopenic obesity.</p>

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Development of a sarcopenic obesity risk prediction model for older adults based on the CHARLS database

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月12日

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  • Biheng Feng,
  • Yuanyuan Qin,
  • Qingjiang Cai,
  • Debin Huang

摘要

Objective

To construct a risk prediction model for sarcopenic obesity in the elderly using different machine learning methods.

Methods

The research data were derived from the CHARLS 2015 national survey data. According to the inclusion and exclusion criteria, 2,375 elderly people were selected and 42 research variables were included. The risk factors were screened by univariate analysis. The variables with statistically significant differences (P < 0.05) were selected using the Boruta feature selection method, resulting in 16 features. A prediction model was then constructed based on six machine learning algorithms. The model was comprehensively evaluated using the area under the receiver operating characteristic curve (AUROC), accuracy, recall, precision, F1 score, and Brier score. The interpretable analysis of the optimal machine learning model was performed with SHAP（Shapley Additive Explanations.

Results

Among the six models, XGBoost had the best comprehensive performance, with an AUC of 0.78, accuracy of 0.89, recall of 0.20, precision of 0.53, F1 score of 0.28, and Brier score of 0.11. The importance analysis of shap features showed that waist circumference, pace and uric acid were important risk factors.

Conclusion

The model constructed by XGBoost machine learning algorithm has the best predictive performance, which may facilitate for early clinical evaluation and prevention of sarcopenic obesity.