Browsing by Author "Zengin, Ela Simay"

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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, Serkan
Objective: 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.
Kinematic Analysis of Gait Parameters in Patients Affected by Neuromyelitis Optica Spectrum Disorders: a Comparative Study
(Sage Publications Ltd, 2024) Ozdogar, Asiye Tuba; Aslan, Taha; Zengin, Ela Simay; Ozakbas, Serkan
Pain in Neuromyelitis Optica Spectrum Disorder: Determination of Prevalence and Characteristics
(Springer Heidelberg, 2025) Ozdogar, Asiye Tuba; Yesiloglu, Pervin; Unal, Gozde Deniz; Engenc, Veysel; Zengin, Ela Simay; Cilingir, Vedat; Ozakbas, Serkan
Introduction The aim was to determine the prevalence and characteristics of pain in people with neuromyelitis optica spectrum disorders (pwNMOSD).
Methods The Nordic Musculoskeletal Questionnaire (NMQ) was used to determine the participants' pain levels and pain localization. The PainDETECT Questionnaire (PD-Q) was used to differentiate between nociceptive and neuropathic pain. The scores <= 12 were considered as presence of musculoskeletal pain (MSP) and > 12 as neuropathic pain (NP). The Preference-Based Multiple Sclerosis Index (PBMSI) was used to measure health-related quality of life. Information such as Expanded Disability Status Scale (EDSS), disease duration and age of the participants were also recorded.
Results The 62 participants included in the study were divided into 3 groups: 14 without pain, 17 with MSP and 31 with NP. There was no difference between the groups in terms of age, disease duration and EDSS scores (p > .05). When the pain distribution was analyzed, the regions with the most pain complaints in the last 12 months were neck (n = 22, 34.9%), foot-ankle (n = 16, 25.4%) and back (n = 15, 25.8%), respectively. When the quality of life of the three groups were compared, there was a difference between PBMSI-Walk, PBMSI-Fatigue and total score.
Conclusion The results of this study showed that the neck, back, and foot-ankle were the most common and most disabling pain areas in pwNMOSD, regardless of the age, disease duration, and EDSS score of the participant. However, there was a difference between the groups in the parameters related to gait, fatigue and total quality of life against NP.