The Differential Diagnosis of Multiple Sclerosis Using Convex Combination of Infinite Kernels

Title:The Differential Diagnosis of Multiple Sclerosis Using Convex Combination of Infinite Kernels

Volume: 16 Issue: 1

Author(s): Yeliz Karaca, Yu-Dong Zhang, Carlo Cattani and Ugur Ayan

Affiliation:

Keywords: Clinical decision support, infinite kernel classification, multiple sclerosis, patient diagnostic.

Abstract: 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.

Cite this article as:

Karaca Yeliz, Zhang Yu-Dong, Cattani Carlo and Ayan Ugur, The Differential Diagnosis of Multiple Sclerosis Using Convex Combination of Infinite Kernels, CNS & Neurological Disorders - Drug Targets 2017; 16 (1) . https://dx.doi.org/10.2174/1871527315666161024142439