Sarcopenia prediction model based on machine learning and SHAP values for community-based older adults with cardiovascular disease in China
Bibliographic Data
| ID | 22072158 |
|---|---|
| Authors | Peil Yu, Xinxin Zhang (0000-0002-2975-6800, Xuzhou Medical College), Guoxuan Sun (Xuzhou Medical College), Ping Zeng (0009-0002-2475-0159, Xuzhou Medical College), Chu Zheng (0000-0003-3309-208X, Xuzhou Medical College), Ke Wang (0000-0002-4808-2637, Xuzhou Medical College, corresponding author) |
| Year | 2025 |
| Volume | 13 |
| Pages | 1527304-1527304 |
| Publication date | 2025-05-21 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2025.1527304 |
| PMID | 40469611 |
| OpenAlex | W4410528330 |
| Language | EN |
| References cited | 52 |
Background: Sarcopenia (SP), is recognized as a complication of cardiovascular disease (CVD), but few relevant diagnostic models have been developed. This study aims to establish an interpretable diagnostic model for the occurrence of SP in older adult CVD patients living in Chinese community-dwelling (CD). Methods: We randomly selected participants with CVD recruited from CHARLS from 2011 to 2015 and divided them into a training set and a test set. In the training set, we processed and screened the predictor variables and addressed the data imbalance by the synthetic minority oversampling technique (SMOTE). Subsequently, we built four machine learning (ML) models to predict SP. After 100 iterations, we selected the best performing model for risk stratification by comparing model discrimination and calibration. Then, we analyzed the relationship between ML risk and SP using scatterplots and logistic regression (LR). Finally, the Shapley's Additive Explanatory Plot (SHAP) illustrates how each feature level affects the predicted probability of SP. Results: ), participants with higher ML scores had a higher risk of SP. In sex-specific subanalyses, BMI, height, age, DBP, HDL, etc. were all significant predictors. Conclusion: This study develops a novel clinically-integrated tool that can be used to easily predict SP in the older adults population with CVD, providing a basis for the development of personalized therapeutic measures
China · Disease · Geography · Machine learning · Physical medicine and rehabilitation · Sarcopenia · Body Composition Measurement Techniques · Computer Science · Medicine · Nutrition and Health in Aging · Nutritional Studies and Diet · Artificial Intelligence · Gerontology · Internal Medicine
| Highly cited | No |
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