BAYESIAN NETWORKS FOR SUB-GROUPS OF MULTIPLE SCLEROSIS

Abstract

In this study, patients with multiple sclerosis "sub-groups" characteristics in relation to detection of a statistically (SPSS) and are provided in the Bayesian network. The main objective of this study, regarding the appearance of MRI lesions in patients with Multiple Sclerosis information and / or EDSS scores to investigate the possible attack of multiple sclerosis subgroups. Bayesian networks, reflects the level of sub-groups in multiple sclerosis patients. Analyzes were conducted to determine the change of these properties. MR images of the input data is discussed for the MS patients, the sub-groups of MS, "Remitting Relapsing Multiple Sclerosis", "Secondary Progressive Multiple Sclerosis" with their patients' clinical brain MR images, brain stem, and the Upper Cervical Regions of the corpus callosum-periventricular lesions created in the information. Multiple Sclerosis is owned by the input data is created correctly identify disease subgroups of MS patients for the number of lesions in MR images and MR image of the three regions for the year for which the information used in the EDSS score. Of MS is RRMS, SPMS correctly identify sub-groups of the brain with Brain Stem, and upper cervical regions of the corpus callosum-periventricular lesions in these three points for the region and / or EDSS score information can be emphasized by using the Bayesian networks play an important role in the analysis.

Keywords

• Multiple Sclerosis (MS), Relapsing Remitting Multiple Sclerosis (RRMS), Secondary Progressive Multiple Sclerosis (SPMS), Bayesian Network

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