Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Machine Learning Methods for Classification Multiple Sclerosis with different stages

Version 1 : Received: 2 June 2021 / Approved: 4 June 2021 / Online: 4 June 2021 (10:59:51 CEST)

How to cite: Barzegar, A.; Barzegar, Y. Machine Learning Methods for Classification Multiple Sclerosis with different stages. Preprints 2021, 2021060137 (doi: 10.20944/preprints202106.0137.v1). Barzegar, A.; Barzegar, Y. Machine Learning Methods for Classification Multiple Sclerosis with different stages. Preprints 2021, 2021060137 (doi: 10.20944/preprints202106.0137.v1).

Abstract

Multiple sclerosis (MS) is a debilitating disease of the brain and spinal cord (central nervous system). In MS, the immune system attacks the protective sheath (myelin) that covers the nerve fibers, causing communication problems between the brain and the rest of the body. Eventually the disease can cause permanent damage or nerve damage. The signs and symptoms of MS are very different and depend on the extent of the nerve damage and which nerves are affected. Some people with severe MS may lose the ability to walk independently or completely, while others may experience a long recovery period without any new symptoms. Most people with MS have a relapsing-remitting illness. They experience periods of new symptoms or recurrences that occur over days or weeks and usually improve somewhat or completely. Following these recurrences, there are periods of recovery that can last for months or even years. In this Project, we used some methods of machine learning in order to evaluate the precision and accuracy of Methods to Predict and classification of Multiple Sclerosis with different stages. In order to calculate accuracy, precision, recall Fscore we used some different method such as Art Fuzzy, SVM, Decision tree to compare the classes two by two. To improve the results we used the method of Adaptive fuzzy optimization. we used two options Genetic algorithm and particle swarm optimization.

Subject Areas

Multiple sclerosis, Machine Learning, precision, Decision tree, Art Fuzzy, SVM

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