Performance Evaluation for Multiple Sclerosis Identification Models Based on MR Imaging and Machine Learning
Date:
The objective of this study is to evaluate the performance of machine learning models based on features extracted from T1 images for identifying Multiple Sclerosis subjects and studying feature importance selected by models.
Machine learning models showed high classification performance between normal and MS. Furthermore, models constructed with data from different age ranges selected different key features thus enabled different precision and sensitivity performance for MS classification. This also suggests evolving effects of disease upon brain during progression. These models might be used as MS classifiers to assist clinicians in decision making.
More information can be found here: [ABSTRACT]