Determination of important predictors for the fifth-year Expanded Disability Status Scale scores of patients with multiple sclerosis using machine learning

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.