Development of restless legs syndrome severity prediction models for people with multiple sclerosis using machine learning

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dc.contributor.authorKaya, Ergi
dc.contributor.authorEmec, Murat
dc.contributor.authorOzdogar, Asiye Tuba
dc.contributor.authorZengin, Eta Simay
dc.contributor.authorKarakas, Hitat
dc.contributor.authorDastan, Seda
dc.contributor.authorOzakbas, Serkan
dc.date.accessioned2026-04-04T18:56:11Z
dc.date.available2026-04-04T18:56:11Z
dc.date.issued2025
dc.departmentİstanbul Bilgi Üniversitesi
dc.description.abstractObjectives: This study aimed to develop an artificial intelligence-supported restless legs syndrome (RLS) severity prediction model for people with multiple sclerosis using machine learning methods. Patients and methods: Twenty-three individuals (14 females, 7 males; mean age: 40.6 +/- 10.9 years; range, 33 to 44 years) with multiple sclerosis with RLS were included in this observational study between March 2022 and March 2023. The International Restless Legs Syndrome Study Group Rating Scale was used to determine the RLS severity of the participants. The age, sex, body mass index, regular exercise habits, disease duration, Expanded Disability Status Scale (EDSS), estimated maximal aerobic capacity (VO2max), Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale, Multiple Sclerosis International Quality of Life Questionnaire, Multiple Sclerosis Walking Scale-12 (MSWS-12), and timed 25-foot walk test were determined as predictive variables. A correlation matrix was created. DecisionTree, RandomForest, and XGBoost machine learning methods were used to develop a model for predicting the RLS severity. Results: According to the obtained correlation matrix, PSQI scores strongly correlated with RLS severity (Pearson r=0.76). Meanwhile, EDSS scores (0.49), MSWS-12 scores (0.45), and disease duration (0.45) showed moderate correlations with RLS. Among the three different meachine learning methods, XGBoost demonstrated the best performance in predicting the severity of RLS, with a mean absolute error of 1.94, mean squared error of 4.58, mean absolute percentage error of 0.0735, and a test accuracy of 92.65%. The results showed that the severity of RLS could be estimated with 92.65% accuracy. Conclusion: This study showed a strong correlation between PSQI scores and RLS severity and that RLS severity could be predicted using machine learning methods.
dc.identifier.doi10.55697/tnd.2025.511
dc.identifier.doi10.55697/tnd.2025.511
dc.identifier.endpage449
dc.identifier.issn1301-062X
dc.identifier.issn1309-2545
dc.identifier.issue4
dc.identifier.startpage440
dc.identifier.urihttps://doi.org/10.55697/tnd.2025.511
dc.identifier.urihttps://hdl.handle.net/11411/10736
dc.identifier.volume31
dc.identifier.wosWOS:001644756900006
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherGalenos Publ House
dc.relation.ispartofTurkish Journal of Neurology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260402
dc.subjectMachine Learning
dc.subjectMultiple Sclerosis
dc.subjectQuality Of Life
dc.subjectRestless Legs Syndrome
dc.subjectSleep Quality
dc.titleDevelopment of restless legs syndrome severity prediction models for people with multiple sclerosis using machine learning
dc.typeArticle

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