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

Ergi Kaya; Murat Emeç; Asiye Tuba Özdoğar; Ela Simay Zengin; Hilal Karakaş; Seda Daştan; Cavid Baba; Mehmet Hilal Özcanhan; Serkan Özakbaş

doi:10.55697/tnd.2025.511

Ergi Kaya¹, Murat Emeç², Asiye Tuba Özdoğar³, Ela Simay Zengin⁴, Hilal Karakaş⁵, Seda Daştan⁵, Cavid Baba⁵, Mehmet Hilal Özcanhan⁶, Serkan Özakbaş⁴

¹Department of Neurology, Dokuz Eylül University Faculty of Medicine, İzmir, Türkiye
²Department of Informatics, İstanbul University, İstanbul, Türkiye
³Department of Physiotherapy, Van Yüzüncü Yıl University Faculty of Medicine, Van, Türkiye
⁴Department of Neurology, İzmir University of Economics, İzmir, Türkiye
⁵Department of Neuosciences, Dokuz Eylül University, Graduate School of Health Sciences, İzmir, Türkiye
⁶Department of Computer Engineering, Dokuz Eylül University, İzmir, Türkiye

Keywords: Machine learning, multiple sclerosis, quality of life, restless legs syndrome, sleep quality.

Abstract

Objectives: 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.

Cite this article as: Kaya E, Emeç M, Özdoğar AT, Zengin ES, Karakaş H, Daştan S, et al. Development of restless legs syndrome severity prediction models for people with multiple sclerosis using machine learning. Turk J Neurol 2025;31(4):443-452. doi: 10.55697/tnd.2025.511.