Neurodegenerative disease management · 2026
Analyzing multiple-sclerosis progression: stage-specific biomarker insights via explainable machine learning.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Akben Selahaddin Batuhan, Bilirim Ayşenur, Akben Cantürk, Türkoğlu Şule Aydın
Аннотация
BACKGROUND: Multiple Sclerosis (MS) is a chronic autoimmune disease where early diagnosis from Clinically Isolated Syndrome (CIS) remains challenging.
METHODS: This study investigates stage-specific biomarkers for CIS-to-MS conversion using explainable machine learning on a 10-year prospective dataset of 273 CIS patients, stratified by EDSS scores (1, 2, 3).
RESULTS: Following data preprocessing and 10-fold cross-validation, Shapley analysis identified clinical, MRI, demographic, and environmental variables. Models achieved high accuracy (EDSS = 1: 89.5% via KNN; EDSS = 2/3: 100% via SVM/Ensemble). Periventricular MRI lesions and oligoclonal bands were primary predictors across all stages. Spinal cord lesions became decisive at EDSS = 3, while motor symptoms were critical for early diagnosis. Lower education and lack of breastfeeding increased MS risk; varicella history showed positive correlation.
CONCLUSION: These AI models effectively identify stage-specific biomarkers, revealing the dynamic importance of MRI findings. The influence of psychosocial and environmental factors underscores a multidisciplinary approach for MS management and early diagnosis.
METHODS: This study investigates stage-specific biomarkers for CIS-to-MS conversion using explainable machine learning on a 10-year prospective dataset of 273 CIS patients, stratified by EDSS scores (1, 2, 3).
RESULTS: Following data preprocessing and 10-fold cross-validation, Shapley analysis identified clinical, MRI, demographic, and environmental variables. Models achieved high accuracy (EDSS = 1: 89.5% via KNN; EDSS = 2/3: 100% via SVM/Ensemble). Periventricular MRI lesions and oligoclonal bands were primary predictors across all stages. Spinal cord lesions became decisive at EDSS = 3, while motor symptoms were critical for early diagnosis. Lower education and lack of breastfeeding increased MS risk; varicella history showed positive correlation.
CONCLUSION: These AI models effectively identify stage-specific biomarkers, revealing the dynamic importance of MRI findings. The influence of psychosocial and environmental factors underscores a multidisciplinary approach for MS management and early diagnosis.