Predicting epilepsy drug treatment outcome is important for treating children with tuberous sclerosis complex (TSC). Here, the best performing model was selected to explore the contribution of the features, using permutation importance (PIMP). An approach similar to PIMP was used to compare the magnetic resonance imaging (MRI) and non-MRI features. The best model multilayer perceptron (MLP) with a hidden layer size of 60 and 30 features selected by F-test achieved the best performance. The results based on 103 children patients showed that some features were more important than others, and MRI features contributed more than non-MRI features in prediction.