Multiple sclerosis is an autoimmune disorder of the central nervous system and potentially the most common cause of neurological disability in young adults. The clinical disease course is highly variable and different multiple sclerosis subtypes can be defined depending on the progression of the severity of the disease. In the early stages, the disease subtype is unknown, and there is no information about how the severity is going to evolve. As there are different treatment options available depending on the progression of the disease, early identification has become highly relevant. Thus, given a new patient, it is important to diagnose the disease subtype. Another relevant information to predict is the expected time to reach a severity level indicating that assistance for walking is required. Given that we have to predict two correlated class variables: disease subtype and time to reach certain severity level, we use multidimensional Bayesian network classifiers because they can model and exploit the relations among both variables. Besides, the obtained models can be validated by the physicians using their expert knowledge due to the interpretability of Bayesian networks. The learning of the classifiers is made by means of a novel multiobjective approach which tries to maximize the accuracy of both class variables simultaneously. The application of the methodology proposed in this paper can help a physician to identify the expected progression of the disease and to plan the most suitable treatment.