Integrating BMAT and FA Imaging for Improved Classification of Multiple Sclerosis: A Machine Learning Perspective

Motivation: This research aims to improve early diagnosis and treatment of MS by integrating cognitive assessment and advanced neuroimaging techniques with machine learning. Goal(s): The goal is to enhance MS classification accuracy by integrating cognitive metrics and neuroimaging data using machine learning algorithms. Approach: This study employs a collaborative approach, combining cognitive assessments, neuroimaging data, and machine learning to classify cognitive changes in MS. Results: The machine learning model achieved an accuracy of 80.89% in classifying MS patients, effectively distinguishing between cognitively preserved and impaired groups based on critical neuroanatomical features identified from imaging data. Impact: This study enhances diagnostic precision for MS, facilitating early intervention and personalized treatment strategies. It encourages further exploration of cognitive decline mechanisms in MS and may inspire similar integrative approaches in other neurological disorders, ultimately improving patient outcomes.