Migraine affects the daily life of millions of people around the world. The most 
well-known disabling symptom associated with this illness is the intense 
headache. Nowadays, there are treatments that can diminish the level of pain. 
OnabotulinumtoxinA (BoNT-A) has become a very popular medication for treating 
migraine headaches in those cases in which other medication is not working, 
typically in chronic migraines. Currently, the positive response to Botox 
treatment is not clearly understood, yet understanding the mechanisms that 
determine the effectiveness of the treatment could help with the development of 
more effective treatments. To solve this problem, this paper sets up a realistic 
scenario of electronic medical records of migraineurs under BoNT-A treatment 
where some clinical features from real patients are labeled by doctors. Medical 
registers have been preprocessed. A label encoding method based on simulated 
annealing has been proposed. Two methodologies for predicting the results of the 
first and the second infiltration of the BoNT-A based treatment are contempled. 
Firstly, a strategy based on the medical HIT6 metric is described, which 
achieves an accuracy over 91%. Secondly, when this value is not available, 
several classifiers and clustering methods have been performed in order to 
predict the reduction and adverse effects, obtaining an accuracy of 85%. Some 
clinical features as Greater occipital nerves (GON), chronic migraine time 
evolution and others have been detected as relevant features when examining the 
prediction models. The GON and the retroocular component have also been 
described as important features according to doctors.