Our purpose is to develop a clinical decision support system to classify the patients’ diagnostics
based on features gathered from Magnetic Resonance Imaging (MRI) and Expanded Disability Status
Scale (EDSS). We studied 120 patients and 19 healthy individuals (not afflicted with MS) have been
studied for this study. Healthy individuals in the control group do not have any complaint or drug use
history. For the kernel trick, efficient performance in non-linear classification, the Convex Combination
of Infinite Kernels model was developed to measure the health status of patients based on features gathered
from MRI and EDSS. Our calculations show that our proposed model classifies the multiple sclerosis
(MS) diagnosis level with better accuracy than single kernel, artificial neural network and other
machine learning methods, and it can also be used as a decision support system for identifying MS
health status of patients.
Keywords: Clinical decision support, infinite kernel classification, multiple sclerosis, patient diagnostic.
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