Volume 12, Number 5

Machine Learning in Early Genetic Detection of Multiple Sclerosis Disease: A Survey

  Authors

Nehal M. Ali¹, Mohamed Shaheen², Mai S. Mabrouk³ and Mohamed A. AboRezka¹, ¹Arab Academy for Science Technology and Maritime Transport, Cairo, Egypt, ²Arab Academy for Science Technology and Maritime Transport, Alexandria, Egypt, ³Misr University for Science and Technology, Cairo, Egypt

  Abstract

Multiple sclerosis disease is a main cause of non-traumatic disabilities and one of the most common neurological disorders in young adults over many countries. In this work, we introduce a survey study of the utilization of machine learning methods in Multiple Sclerosis early genetic disease detection methods incorporating Microarray data analysis and Single Nucleotide Polymorphism data analysis and explains in details the machine learning methods used in literature. In addition, this study demonstrates the future trends of Next Generation Sequencing data analysis in disease detection and sample datasets of each genetic detection method was included .in addition, the challenges facing genetic disease detection were elaborated.

  Keywords

Multiple sclerosis, Machine learning, Microarray, Single Nucleotide Polymorphism, early disease detection, Next Generation Sequencing.