Keywords: Multiple Sclerosis, Neuroscience
Motivation: Prediction of achieving no evidence of disease activity (NEDA) status in relapsing-remitting multiple sclerosis (RRMS) patients on oral disease-modifying therapies (DMTs) is crucial for clinical decision-making and patient management.
Goal(s): To develop a clinical and regional gray matter atrophy variables-based machine learning model to predict achieving NEDA status in RRMS patients on oral DMTs.
Approach: Composite performance scores were calculated based on eight standard performance metrics ranking to screen out the optimal model among five models, and risk stratification analysis was performed.
Results: The logistic regression (LR) model demonstrated the best performance and can distinguish well between high- and low-risk RRMS individuals.
Impact: Baseline clinical and regional gray matter atrophy variables-based machine learning models can help physicians predict NEDA status in RRMS patients on oral DMTs for clinical decision-making and patient management.
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