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Background <p>Individuals with chronic diseases are at higher risk of sarcopenia, and precise prediction is essential for its prevention. This study aims to develop a risk scoring model using longitudinal data to predict the probability of sarcopenia in this population over next 3–5&#xa0;years, thereby enabling early warning and intervention.</p> Methods <p>Using data from a nationwide survey initiated in 2011, we selected patient data records from wave 1 (2011–2012) and follow-up data from wave 3 (2015–2016) as the study cohort. Retrospective data collection included demographic information, health conditions, and biochemical markers. After excluding records with missing values, a total of 2891 adults with chronic conditions were enrolled. Sarcopenia was assessed based on the Asian Working Group for Sarcopenia (AWGS) 2019 guidelines. A generalized linear mixed model (GLMM) with random effects and diverse machine learning models were utilized to explore feature contributions to sarcopenia risk. The Recursive Feature Elimination (RFE) algorithm was employed to optimize the full Multilayer Perceptron (MLP) model and develop an online application tool.</p> Results <p>Among total population, 580 (20.1%) individuals were diagnosed with sarcopenia in wave 1 (2011–2012), and 638 (22.1%) were diagnosed in wave 3 (2015–2016), while 2165 (74.9%) individuals were not diagnosed with sarcopenia across the study period. MLP model, performed better than other three classic machine learning models, demonstrated a ROC AUC of 0.912, a PR AUC of 0.401, a sensitivity of 0.875, a specificity of 0.844, a Kappa value of 0.376, and an F1 score of 0.44. According to MLP model-based SHapley Additive exPlanations (SHAP)&#xa0;scoring, weight, age, BMI, height, total cholesterol, PEF, and gender were identified as the most important features of chronic disease individuals for sarcopenia. Using the RFE algorithm, we selected six key variables—weight, age, BMI, height, total cholesterol, and gender—achieving an ROC AUC of about 0.9 for the online application tool.</p> Conclusion <p>We developed an MLP machine learning model that incorporates only six easily accessible variables, enabling the prediction of sarcopenia risk in individuals with chronic diseases. Additionally, we created a practical online application tool to assist in decision-making and streamline clinical assessments.</p>

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Intelligent predictive risk assessment and management of sarcopenia in chronic disease patients using machine learning and a web-based tool

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月29日

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  • Ke Rong,
  • Gu li jiang Yi ke ran,
  • Changgui Zhou,
  • Xinglin Yi

摘要

Background

Individuals with chronic diseases are at higher risk of sarcopenia, and precise prediction is essential for its prevention. This study aims to develop a risk scoring model using longitudinal data to predict the probability of sarcopenia in this population over next 3–5 years, thereby enabling early warning and intervention.

Methods

Using data from a nationwide survey initiated in 2011, we selected patient data records from wave 1 (2011–2012) and follow-up data from wave 3 (2015–2016) as the study cohort. Retrospective data collection included demographic information, health conditions, and biochemical markers. After excluding records with missing values, a total of 2891 adults with chronic conditions were enrolled. Sarcopenia was assessed based on the Asian Working Group for Sarcopenia (AWGS) 2019 guidelines. A generalized linear mixed model (GLMM) with random effects and diverse machine learning models were utilized to explore feature contributions to sarcopenia risk. The Recursive Feature Elimination (RFE) algorithm was employed to optimize the full Multilayer Perceptron (MLP) model and develop an online application tool.

Results

Among total population, 580 (20.1%) individuals were diagnosed with sarcopenia in wave 1 (2011–2012), and 638 (22.1%) were diagnosed in wave 3 (2015–2016), while 2165 (74.9%) individuals were not diagnosed with sarcopenia across the study period. MLP model, performed better than other three classic machine learning models, demonstrated a ROC AUC of 0.912, a PR AUC of 0.401, a sensitivity of 0.875, a specificity of 0.844, a Kappa value of 0.376, and an F1 score of 0.44. According to MLP model-based SHapley Additive exPlanations (SHAP) scoring, weight, age, BMI, height, total cholesterol, PEF, and gender were identified as the most important features of chronic disease individuals for sarcopenia. Using the RFE algorithm, we selected six key variables—weight, age, BMI, height, total cholesterol, and gender—achieving an ROC AUC of about 0.9 for the online application tool.

Conclusion

We developed an MLP machine learning model that incorporates only six easily accessible variables, enabling the prediction of sarcopenia risk in individuals with chronic diseases. Additionally, we created a practical online application tool to assist in decision-making and streamline clinical assessments.