Franklin Parrales–BravoVíctor Gustavo Gómez RodríguezLorenzo Cevallos-Torres2026-03-242026-03-24202410.1109/icecet61485.2024.10698587https://doi.org/10.1109/icecet61485.2024.10698587https://andeanlibrary.org/handle/123456789/100053Citaciones: 2Since other treatments often do not work for treating migraine headaches, Onabotulinumtoxin-A, or BoNT-A, has gained a lot of popularity, especially in chronic migraines. The treatment consists of multiple sessions of drug injections. At the moment, it's unclear why BoNT-A therapy produces a beneficial reaction. In order to address this issue, the present work explores the use of Multidimensional Bayesian Classifiers (MBC) for training a multi-stage prediction model. It is carried out in a realistic setting by considering retrospective data from migraine patients receiving treatment with BoNT-A. As far as we are aware, there are no known studies using MBC in this domain. The model has achieved an average accuracy of 79.45%, 82.57%, and 77.35 % when predicting responses to the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1^{st}, 2^{nd}$</tex>, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3^{rd}$</tex> stages of treatment, respectively. It has also achieved similar values of sensitivity and specificity, enabling medical professionals to get a panoramic prediction of a patient's reaction to therapy and base their decisions accordingly. When looking at the prediction models, some clinical features have been identified as important, such as the number of days with a headache, the anesthetic blockade of the greater occipital nerve (GON), and others. Doctors have also described these features as significant aspects.enBayesian probabilityComputer scienceArtificial intelligenceMigraineClassifier (UML)Machine learningPattern recognition (psychology)Naive Bayes classifierMedicineInternal medicineSupport vector machinearticle