Machine Learning Classification of Cognitive Status in Community-Dwelling Sarcopenic Women: A SHAP-Based Analysis of Physical Activity and Anthropometric Factors

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dc.contributor.authorGormez, Yasin
dc.contributor.authorYagin, Fatma Hilal
dc.contributor.authorAygun, Yalin
dc.contributor.authorAlzakari, Sarah A.
dc.contributor.authorAlhussan, Amel Ali
dc.contributor.authorAghaei, Mohammadreza
dc.date.accessioned2026-04-04T13:30:59Z
dc.date.available2026-04-04T13:30:59Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractBackground and Objectives: Sarcopenia, characterized by progressive loss of skeletal muscle mass and function, has increasingly been recognized not only as a physical health concern but also as a potential risk factor for cognitive decline. This study investigates the application of machine learning algorithms to classify cognitive status based on Mini-Mental State Examination (MMSE) scores in community-dwelling sarcopenic women. Materials and Methods: A dataset of 67 participants was analyzed, with MMSE scores categorized into severe (<= 17) and mild (>17) cognitive impairment. Eight classification models-MLP, CatBoost, LightGBM, XGBoost, Random Forest (RF), Gradient Boosting (GB), Logistic Regression (LR), and AdaBoost-were evaluated using a repeated holdout strategy over 100 iterations. Hyperparameter optimization was performed via Bayesian optimization, and model performance was assessed using metrics including weighted F1-score (w_f1), accuracy, precision, recall, PR-AUC, and ROC-AUC. Results: Among the models, CatBoost achieved the highest w_f1 (87.05 +/- 2.85%) and ROC-AUC (90 +/- 5.65%), while AdaBoost and GB showed superior PR-AUC scores (92.49% and 91.88%, respectively), indicating strong performance in handling class imbalance and threshold sensitivity. SHAP (SHapley Additive exPlanations) analysis revealed that moderate physical activity (moderatePA minutes), walking days, and sitting time were among the most influential features, with higher physical activity associated with reduced risk of cognitive impairment. Anthropometric factors such as age, BMI, and weight also contributed significantly. Conclusions: The results highlight the effectiveness of boosting-based models in capturing complex patterns in clinical data and provide interpretable evidence supporting the role of modifiable lifestyle factors in cognitive health. These findings suggest that machine learning, combined with explainable AI, can enhance risk assessment and inform targeted interventions for cognitive decline in older women.
dc.description.sponsorshipPrincess Nourah bint Abdulrahman University [PNURSP2025R716]
dc.description.sponsorshipPrincess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2025R716), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
dc.identifier.doi10.3390/medicina61101834
dc.identifier.issn1010-660X
dc.identifier.issn1648-9144
dc.identifier.issue10
dc.identifier.orcid0000-0001-7530-7961
dc.identifier.orcid0000-0002-9848-7958
dc.identifier.orcid0000-0001-8276-2030
dc.identifier.orcid0000-0002-1018-657X
dc.identifier.orcid0000-0001-5735-3825
dc.identifier.pmid41155821
dc.identifier.scopus2-s2.0-105020076743
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/medicina61101834
dc.identifier.urihttps://hdl.handle.net/11616/108503
dc.identifier.volume61
dc.identifier.wosWOS:001603821800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofMedicina-Lithuania
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjectmachine learning
dc.subjectcognitive impairment
dc.subjectphysical activity
dc.subjectSHAP
dc.subjectwomen
dc.subjectsarcopenia
dc.subjectsedentary behavior
dc.subjectMMSE
dc.titleMachine Learning Classification of Cognitive Status in Community-Dwelling Sarcopenic Women: A SHAP-Based Analysis of Physical Activity and Anthropometric Factors
dc.typeArticle

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