Developing a Model for Predicting Multiple Sclerosis Activity Based On Machine Learning

To control disability and disease progression, accurate diagnosis of multiple sclerosis is essential, as is differential diagnosis. The application of artificial intelligence in the treatment of MS, especially in the field of neuroradiology, is becoming increasingly popular. AI is well suited for predicting clinical disability as well as the long-term benefit and safety of in MS patients. It is worth noting the rather high potential effectiveness of the proposed in managing patients in practice. All implemented machine learning models show sufficient levels of quality metrics. The model of MS course prediction (gradient bousting algorithm) - more than 80% for each of the metrics (Accuracy, Precision, Recall, Fl-score). However, it should be noted that these algorithm performance parameters can be improved by collecting a significant amount of additional data, which would allow to expand the datasets, test the predictive power with respect to MS flow, and improve and stabilize the quality metrics of the models.