ArticleBMC geriatrics2026
Construction and evaluation of a CatBoost-based machine learning model for sarcopenia prediction using bioelectrical impedance analysis (BIA) and handgrip strength (HGS) from a nationally representative dataset of Korean older adults.
Article in BMC geriatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
This study aimed to develop and validate an explainable machine learning model for predicting sarcopenia in older adults using nationally representative data. Using the 2022-2023 Korea National Health and Nutrition Examination Survey (KNHANES; n = 2,721, ≥ 65 y), we constructed a data-driven framework integrating demographic, anthropometric, biochemical, and bioelectrical impedance analysis (BIA) variables. Sarcopenia was defined by the 2019 Asian Working Group for Sarcopenia (AWGS) criteria, requiring both low appendicular skeletal muscle mass and reduced handgrip strength. Five supervised algorithms-logistic regression, random forest, XGBoost, LightGBM, and CatBoost-were trained using tenfold cross-validation, with sex-specific models to address biological heterogeneity. The optimized CatBoost model showed the highest and most balanced performance. For men, accuracy = 0.912, sensitivity = 0.833, specificity = 0.920, F1 = 0.645, and AUC = 0.930; for women, accuracy = 0.895, sensitivity = 0.567, specificity = 0.932, F1 = 0.523, and AUC = 0.928. Feature importance analysis based on total feature importance gain indicated that BIA-derived water indices-intracellular water (ICW), extracellular water (ECW), and total body water (TBW)-together with appendicular lean soft tissue mass accounted for 72.4% (men) and 70.8% (women) of the overall model importance. SHapley Additive exPlanations (SHAP) identified ICW and phase angle (PhA) as top predictors. In men, the mean absolute SHAP value for ICW was 0.660 and for PhA was 0.282, while in women, the corresponding values were 0.750 for ICW and 0.536 for PhA, confirming their major contribution to model output. In conclusion, this CatBoost-based, SHAP-interpretable model effectively integrates BIA-derived physiological parameters with clinical data, providing accurate and sex-specific sarcopenia prediction for scalable early screening.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.