Severity classification of multiple sclerosis disease: A rough set-based method

Abstract

Multiple sclerosis (MS) is among the world’s most common neurologic disorder. Severity classification of MS disease is necessary for treatment and medication dosage decisions and to understand the disease progression. To the best of authors’ knowledge, this is the first study for the severity classification of MS disease. In this study, Rough set (RS) approach is applied to discern the three classes (mild, moderate, and severe) of the severity of MS disease. Furthermore, the performance of the RS approach is compared with Machine learning (ML) classifiers namely, random forest, K-nearest neighbour, and support vector machine. The performance is evaluated on the dataset acquired from Multiple sclerosis outcome assessments consortium (MSOAC), Arizona, US. The weighted average accuracy, precision, recall, and specificity values for the RS approach are found to be 84.04%, 76.99%, 76.75%, and 83.84% respectively. However, among the ML classifiers, the performance of random forest classifier is found best for which the weighted average accuracy, precision, recall, and specificity values are 62.19 %, 52.65 %, 56.84 %, and 59.87 % respectively. The RS approach is found much superior to ML classifiers and may be used for MS disease severity classification. This study may be helpful for the clinicians to assess the severity of the MS patients and to take medication and dosage decisions.