Volume 12, Number 5

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


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


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.


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