Determination of important predictors for the fifth-year Expanded Disability Status Scale scores of patients with multiple sclerosis using machine learning
İlknur Buçan Kırkbir1,2
, Burçin Kurt2, Cavit Boz3, Murat Terzi4
1Department of Public Health Nursing, Karadeniz Technical University, Faculty of Heath Science, Trabzon, Türkiye
2Department of Biostatistics and Medical Informatics, Karadeniz Technical University, Institute of Medical Science, Trabzon, Türkiye
3Department of Neurology, Karadeniz Technical University Faculty of Medicine, Trabzon, Türkiye
4Department of Neurology, Ondokuz Mayıs University Faculty of Medicine, Samsun, Türkiye
Keywords: Feature selection, machine learning, multiple sclerosis.
Abstract
Objectives: This study aimed to determine important predictors of fifth-year Expanded Disability Status Scale (EDSS) scores in multiple sclerosis (MS) patients using machine learning.
Patients and methods: In this retrospective study, the XGBoost basic model was developed to predict five-year EDSS scores in 1,000 patients (317 males, 683 females; mean age: 43.4±10.9 years; range, 18 to 76 years) with MS between January 1999 and December 2020. Patients were categorized based on the initial symptoms of MS onset: brainstem symptoms, optic symptoms, spinal symptoms, or supratentorial symptoms. In the next stage, important predictors of fifth-year EDSS scores were determined and ranked by their importance using the SHAP (SHapley Additive exPlanations) algorithm, which is a machine learning method.
Results: For patients with optic symptoms at onset, second-year EDSS scores, age, and first-year pyramidal functions were identified as the most important variables, respectively. In contrast, for those with spinal symptoms at onset, second-year pyramidal functions, age, and second-year ambulation were important predictors. In the patients with brainstem symptoms at onset, age, first-year EDSS scores, and first-year bowel and bladder functions were determined as important variables. Additionally, for patients with supratentorial symptoms at onset, second-year pyramidal functions, second-year EDSS scores, and age were the top predictors.
Conclusion: The results provided valuable insights into predictors of fifth-year EDSS scores in patients with MS grouped by their initial symptoms. Our findings indicate that the ranking of importance of functional system evaluations varies among patients with MS based on their initial symptoms, with age as a significant predictor for all symptom groups.
Cite this article as: Buçan Kırkbir İ, Kurt B, Boz C, Terzi M. Determination of important predictors for the fifth-year Expanded Disability Status Scale scores of patients with multiple sclerosis using machine learning. Turk J Neurol 2024;30(3):157-166. doi: 10.55697/tnd.2024.107.
The data that support the findings of this study are available from the corresponding author upon reasonable request.
The study protocol was approved by the Karadeniz Technical University Scientific Research Ethics Committee (date: 12.10.2020, no: 2020- 232). The study was conducted in accordance with the principles of the Declaration of Helsinki.
Since this was a retrospective study using retrospectively obtained information, no consent form was obtained.
Conceptualization, methodology, formal analysis, software, writing-original draft preparation: İ.B.K.; Conceptualization, methodology, supervision: B.K.; Conceptualization, data gathering: C.B.; Data gathering: M.T.
The authors declared no conflicts of interest with respect to the authorship and/or publication of this article.
The authors received no financial support for the research and/or authorship of this article.