A machine learning approach to predict foot care self-management in older adults with diabetes

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dc.authorid0000-0002-8993-674X
dc.contributor.authorOzgur, Su
dc.contributor.authorMum, Serpilay
dc.contributor.authorBenzer, Hilal
dc.contributor.authorToran, Meryem Kocaslan
dc.contributor.authorToygar, Ismail
dc.date.accessioned2025-04-22T13:27:54Z
dc.date.available2025-04-22T13:27:54Z
dc.date.issued2024
dc.departmentEge Üniversitesi, Tıp Fakültesi, Dahili Bilimler Bölümü, Göğüs Hastalıkları Ana Bilim Dalı
dc.description.abstractBackgroundFoot care self-management is underutilized in older adults and diabetic foot ulcers are more common in older adults. It is important to identify predictors of foot care self-management in older adults with diabetes in order to identify and support vulnerable groups. This study aimed to identify predictors of foot care self-management in older adults with diabetes using a machine learning approach.MethodThis cross-sectional study was conducted between November 2023 and February 2024. The data were collected in the endocrinology and metabolic diseases departments of three hospitals in Turkey. Patient identification form and the Foot Care Scale for Older Diabetics (FCS-OD) were used for data collection. Gradient boosting algorithms were used to predict the variable importance. Three machine learning algorithms were used in the study: XGBoost, LightGBM and Random Forest. The algorithms were used to predict patients with a score below or above the mean FCS-OD score.ResultsXGBoost had the best performance (AUC: 0.7469). The common predictors of the models were age (0.0534), gender (0.0038), perceived health status (0.0218), and treatment regimen (0.0027). The XGBoost model, which had the highest AUC value, also identified income level (0.0055) and A1c (0.0020) as predictors of the FCS-OD score.ConclusionThe study identified age, gender, perceived health status, treatment regimen, income level and A1c as predictors of foot care self-management in older adults with diabetes. Attention should be given to improving foot care self-management among this vulnerable group.
dc.identifier.citationOzgur, S., Mum, S., Benzer, H., Toran, M. K., & Toygar, I. (2024). A machine learning approach to predict foot care self-management in older adults with diabetes. Diabetology and Metabolic Syndrome, 16(1), 244-9.
dc.identifier.doi10.1186/s13098-024-01480-z
dc.identifier.endpage9
dc.identifier.issn17585996
dc.identifier.issue1
dc.identifier.pmid39375790
dc.identifier.scopus2-s2.0-85206080666
dc.identifier.scopusqualityQ2
dc.identifier.startpage1
dc.identifier.urihttps://doi.org/10.1186/s13098-024-01480-z
dc.identifier.urihttps://hdl.handle.net/11454/117143
dc.identifier.volume16
dc.identifier.wosWOS:001327738500001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorOzgur, Su
dc.institutionauthorid0000-0002-8993-674X
dc.language.isoen
dc.publisherBMC
dc.relation.ispartofDiabetology & Metabolic Syndrome
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectFoot care
dc.subjectOlder adults
dc.subjectSelf-management
dc.subjectMachine learning
dc.subjectDiabetes
dc.titleA machine learning approach to predict foot care self-management in older adults with diabetes
dc.typeArticle

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