Computational Intelligence Technique for Prediction of Multiple Sclerosis Based on Serum Cytokines

Abstract

Multiple sclerosis (MS) is a neurodegenerative disease characterized by lesions in the central nervous system (CNS). Inflammation and demyelination are the leading causes of neuronal death and brain lesions formation. The immune reactivity is believed to be essential in the neuronal damage in MS. Cytokines play important role in differentiation of Th cells and recruitment of auto-reactive B and T lymphocytes that leads to neuron demyelination and death. Several cytokines have been found to be linked with MS pathogenesis. In the present study, serum level of eight cytokines (IL-1β, IL-2, IL-4, IL-8, IL-10, IL-13, IFN-γ, and TNF-α) was analyzed in USA and Russian MS to identify predictors for the disease. Further, the model was extended to classify MS into remitting and non-remitting by including age, gender, disease duration, Expanded Disability Status Scale (EDSS) and Multiple Sclerosis Severity Score (MSSS) into the cytokines datasets in Russian cohorts. The individual serum cytokines data for the USA cohort was generated by Z score percentile method using R studio, while serum cytokines of the Russian cohort were analyzed using multiplex immunoassay. Datasets were divided into training (70%) and testing (30%). These datasets were used as an input into four machine learning models (support vector machine, decision tree, random forest, and neural networks) available in R programming language. Random forest model was identified as the best model for diagnosis of MS as it performed remarkable on all the considered criteria i.e., Gini, accuracy, specificity, AUC, and sensitivity. RF model also performed best in predicting remitting and non-remitting MS. The present study suggests that the concentration of serum cytokines could be used as prognostic markers for the prediction of MS.

Serum and Cerebrospinal Fluid Cytokine Biomarkers for Diagnosis of Multiple Sclerosis

“…Changes in a large number of cytokines, a soluble biomarkers of inflammation and leukocyte activation, in serum and CSF were demonstrated [ 6 , 12 ]. These data suggest that some cytokines could have a diagnostic and prognostic value in MS [ 10 ]. We have previously applied the machine learning models to diagnose MS, using limited data on cytokines affected in the serum of MS [ 10 ], which produced relatively low confidence result.…”

“…These data suggest that some cytokines could have a diagnostic and prognostic value in MS [ 10 ]. We have previously applied the machine learning models to diagnose MS, using limited data on cytokines affected in the serum of MS [ 10 ], which produced relatively low confidence result. Therefore, we expanded the number of cytokines (total of 45), which included interleukins, growth factors, and chemokines, so the computational data analysis would identify the group of biomarkers differentiating MS with a high level of confidence.…”

“…We found that twenty-two and twenty cytokines in CSF and serum, respectively, were affected (strong correlation, r > 0.5) in MS as compared to non-MS. All five models have shown the high accuracy of MS diagnosis, ranging between 90-99%. In our previous report [ 10 ], four models (SVM, DT, RF, and KNN) were applied and only RF model has shown the accuracy of 90.91% with limited cytokine dataset (eight cytokines). In the present analysis, the RF model has shown the accuracy of ≥92%, which is similar to our previous report [ 10 ].…”

“…As an alternative approach, the machine learning methods were successfully employed for the prediction of multiple diseases, including MS [ 7 – 9 ]. Using this approach, we have previously identified serum cytokines which could be used for the diagnosis of MS [ 10 ].…”

“…Serum samples are often collected as a routine procedure for the diagnosis of MS, and they could be used to identify the disease biomarkers. We have shown multiple cytokines affected in MS [ 6 , 10 , 12 ]; however, these studies had limited patient group and restricted number of cytokines analyzed. In addition, we employed several machine-learning approaches to identify cytokines, which could provide high accuracy of MS diagnosis.…”