Comparison of disability score estimation in multiple sclerosis patients with artificial neural network and decision tree models

Background: Multiple Sclerosis (MS) is one of the most debilitating disease among young adults. Understanding the disability score (Expanded Disability Status Scale (EDSS)) of these patients is helpful in choosing their treatment process. Calculating EDSS takes a lot of time for Neurologists, so having a way to estimate EDSS can be helpful. This study aimed to estimate the EDSS score of MS patients using statistical models including Artificial Neural Network (ANN) and Decision Tree (DT) models. Methods: This cross-sectional study was performed on MS registry study data of Kermanshah province from April 2017 to November 2018. From the total data available in the registry system, The 12 variables including demographic information, information about MS disease and their EDSS score were extracted. EDSS scores were also estimated using ANN and DT models. The performance of the models was compared in terms of estimation error, correlation and mean of an estimated score. Data were analyzed using Weka software version 3.9.2 and SPSS software version 25 with a significance level of 0.05. Results: In this study, 353 people were studied. The mean age of the patients was 36.47±9.1 years, the mean age of onset was 9.2±30.34 years, the mean duration of the disease was 6.20±5.7 years and the mean EDSS score was 2.46±1.8. Estimation errors in the DT model were lower than in the ANN model. The real EDSS score was significantly correlated with scores estimated by DT (r=0.571) and ANN (r=0.623). The mean EDSS estimated by the DT model (2.46±1.1) was not significantly different from the real EDSS mean (P=0.621) but the mean EDSS estimated by the ANN model (2.87±1.3) was significantly higher than the real EDSS mean. (P<0.05). Conclusion: The DT model could better estimate the EDSS score of MS patients than the ANN model and made predictions that were closer to the actual EDSS scores. Therefore, the DT model can accurately estimate the EDSS score of MS patients.