Browsing by Author "Ozakbas, Serkan"
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Conference Object The 6-Minute Threshold: Tracking Three-Year Disability and Cognition in Multiple Sclerosis(Sage Publications Ltd, 2025) Unal, Gozde Deniz; Caliskan, Can; Zengin, Ela; Ozakbas, SerkanConference Object Age Matters: Distinct Disability Patterns in Multiple Sclerosis Patients Initiating Therapy After the Age of 50(Sage Publications Ltd, 2025) Zengin, Ela; Caliskan, Can; Simsek, Yasemin; Unal, Gozde Deniz; Ozakbas, SerkanConference Object Bayesian Network to Generate Representative Longitudinal Data in Multiple Sclerosis(Sage Publications Ltd, 2025) Qiang, Zhe; Iqbal, Hassam; Sharmin, Sifat; Ozakbas, Serkan; Zakaria, Magd Fouad; Etemadifar, Masoud; Kalincik, TomasConference Object Characterizing Pain in Neuromyelitis Optica Spectrum Disorder: Prevalence and Clinical Features(Sage Publications Ltd, 2025) Yesiloglu, Pervin; Ozdogar, Asiye Tuba; Unal, Gozde Deniz; Engenc, Veysel; Zengin, Ela; Cilingir, Vedat; Ozakbas, SerkanConference Object Cladribine as an Exit Strategy in People with MS Over the Age of 50(Sage Publications Ltd, 2025) Roos, Izanne; Sharmin, Sifat; Mueller, Jannis; Horakova, Dana; Havrdova, Eva; Ozakbas, Serkan; Kalincik, TomasConference Object Clinical Characteristics and Disease Progression in Multiple Sclerosis: A Comparison of Late Middle-Age Onset vs Late Adulthood Onset(Sage Publications Ltd, 2025) Ozakbas, Serkan; Simsek, Yasemin; Caliskan, Can; Ozdogar, Asiye TubaConference Object Cognitive Decline as the First Sign of Progression in Multiple Sclerosis: A Comparative Analysis of Cognitive and Physical Decline(Sage Publications Ltd, 2025) Ozakbas, Serkan; Basaran, Tuncay; Unal, Gozde Deniz; Caliskan, Can; Aydin, Esra; Kara, Irem; Zengin, ElaConference Object Cognitive Profiles of Relapsing Multiple Sclerosis Patients With Progression Independent of Relapse Activity Versus Non-NEDA Status(Sage Publications Ltd, 2025) Alizada, Said; Ozdogar, Asiye Tuba; Samadzade, Ulvi; Caliskan, Can; Ozakbas, SerkanConference Object Cognitive Reserve at Diagnosis Predicts Nine-Year Cognitive Outcomes in Multiple Sclerosis(Sage Publications Ltd, 2025) Kara, Irem; Zengin, Ela; Unal, Gozde Deniz; Ozakbas, SerkanConference Object Cognitive Stability Over Three Years Despite Loss of Ambulation in Progressive Multiple Sclerosis With Preserved Upper Limb Function(Sage Publications Ltd, 2025) Samadzade, Ulvi; Unal, Gozde Deniz; Caliskan, Can; Aydin, Esra; Ozakbas, SerkanConference Object Comparative Effectiveness of Rituximab and Immunosuppressants in Neuromyelitis Optica Spectrum Disorder: a Retrospective Analysis of International Registry Data(Sage Publications Ltd, 2025) Huang, Yishi; Engels, Daniel; Shaygannejad, Vahid; Horakova, Dana; Havrdova, Eva; Ozakbas, Serkan; Kalincik, TomasConference Object Determinants of Walking Capacity in People With MS: The Role of Balance Confidence and Cognitive Function(Sage Publications Ltd, 2025) Abasiyanik, Zuhal; Unal, Gozde Deniz; Kara, Irem; Ozakbas, SerkanArticle Development of a Machine Learning Model to Predict the Expanded Disability Status Scale in Multiple Sclerosis Patients(Elsevier Sci Ltd, 2026) Ozdogar, Asiye Tuba; Emec, Murat; Kaya, Ergi; Zengin, Ela Simay; Ozcanhan, Mehmet Hilal; Ozakbas, SerkanObjective: The assessment of disability in multiple sclerosis (MS) patients is crucial for treatment decisions and prognosis estimation. The Expanded Disability Status Scale (EDSS) provides a standardized way to quantify disability in MS. However, predicting EDSS scores can be challenging due to the complex and heterogeneous nature of the disease. Machine learning techniques offer a promising approach to predict EDSS scores based on various patient characteristics. Methods: 231 people with MS (pwMS) who had an assessment of physical, psychosocial, and cognitive functions in three timelines (baseline (T0), first year (T1), and second year (T2)) were enrolled. The dataset used for the study consists of 126 features. Feature selection was based on feature saliency and correlation analysis. Three machine learning models -XGBoost, Random Forest, and Linear Regression -were trained on the selected features. Hyperparameter tuning was also carried out on the models. Model performance was evaluated using standard evaluation metrics, including MAE, MSE, and R2. Results: The Machine Learning model based on the XGBoost algorithm performed best in predicting EDSS scores (T2). The MAE value obtained with the XGBoost model is 0.2361, the MSE value is 0.2408, and the R2 value is 0.9705. These results indicate that XGBoost's predictive ability on the current dataset is promising. Conclusion: Our study demonstrates the feasibility of using machine learning techniques to predict EDSS scores in MS patients. The developed models show promising performance and have the potential to enhance clinical decision-making and patient management in MS care.Article Development of Restless Legs Syndrome Severity Prediction Models for People with Multiple Sclerosis Using Machine Learning(Galenos Publishing House, 2025) Kaya, Ergi; Emec, Murat; Ozdogar, Asiye Tuba; Zengin, Eta Simay; Karakas, Hitat; Dastan, Seda; Ozakbas, SerkanObjectives: 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.Conference Object Do Multiple Sclerosis Patients Perceive the Effectiveness of Relapse Treatment? A Comprehensive Evaluation Through the Expanded Disability Status Scale and Patient Determined Disease Steps Correlation(Sage Publications Ltd, 2025) Caliskan, Can; Unal, Gozde Deniz; Zengin, Ela; Ozakbas, SerkanConference Object Earlier Decline in Upper Limb Function Than Gait in Multiple Sclerosis: A Five-Year Longitudinal Study(Sage Publications Ltd, 2025) Unal, Gozde Deniz; Samadzade, Ulvi; Aydin, Esra; Caliskan, Can; Ozakbas, SerkanConference Object Elevated IgM Index Is Linked to More Severe Cognitive Impairment in Multiple Sclerosis(Sage Publications Ltd, 2025) Samadzade, Ulvi; Caliskan, Can; Unal, Gozde Deniz; Kara, Irem; Aydin, Esra; Ozakbas, SerkanConference Object Enhancing Fine Motor Assessment: The 9HPT-Extended as a Superior Alternative to the Standard 9HPT(Sage Publications Ltd, 2025) Unal, Gozde Deniz; Aydin, Esra; Caliskan, Can; Samadzade, Ulvi; Ozdogar, Asiye Tuba; Kahraman, Turhan; Ozakbas, SerkanConference Object Evaluating the Ability of PDDS to Reflect Motor Function in Multiple Sclerosis: Comparison With EDSS and the Timed Up and Go Test(Sage Publications Ltd, 2025) Unal, Gozde Deniz; Caliskan, Can; Samadzade, Ulvi; Ozakbas, SerkanConference Object Evaluation of Disability in Individuals with Multiple Sclerosis Using Clinician-Rated and Patient-Reported Measures: A Cross-Sectional and Retrospective Study(Sage Publications Ltd, 2025) Caliskan, Can; Simsek, Yasemin; Samadzade, Ulvi; Ozakbas, Serkan
