A novel decision tree approach to predict the probability of conversion to multiple sclerosis in Iranian patients with optic neuritis

Background: assessing the risk of conversion to multiple sclerosis (MS) in patients with optic neuritis (ON) has been the topic of numerous studies. However, since the risk factors differ from population to population, the extension of conclusions is a matter of debate. This study focused on the Iranian patients with optic neuritis and assessed the probability of conversion to multiple sclerosis by using a machine-based learning decision tree.

Methods: in this retrospective, observational study the medical records of patients with optic neuritis from 2008 to 2018 were reviewed. Baseline vision, the treatment modality, magnetic resonance imaging (MRI) findings, and patients' demographics were gathered to evaluate the odds of each factor for conversion to MS. The decision tree was then obtained from these data based on their specificity and sensitivity to predict the probability of conversion to MS.

Results: the overall conversion rate to MS was 42.2% (117/277). 63.1 percent of patients had abnormal MRIs at baseline. The presence of white matter plaque had the highest odds for the conversion followed by the positive history of optic neuritis attack and gender. The regression tree showed that the presence of plaque was the most important predicting factor that increased the probability of conversion from 16 to 51 percent.

Conclusion: the decision tree could predict the probability of conversion to MS by considering multiple risk factors with acceptable precision.