Abstract: Migraine is a neurovascular disorder with a prevalence that exceeds 1 billion individuals worldwide, but it has long been recognized to have unique diagnostic challenges due to its heterogeneous pathophysiology and dependence on subjective assessments. As has been extensively documented by a number of international law bodies, migraine in the workplace has been identified as a significant issue that requires urgent attention. Migraine defined by episodic, unilateral and debilitating symptoms including aura, nausea incurs a high socioeconomic burden in disability. Mechanisms such as altered cortical excitability and trigeminal system activation, although researched to a high extent, are still inadequately understood. Deep learning and machine learning (ML) hold tremendous potential for transforming diagnosis and classification of migraine. This study evaluates several machine learning (ML) models such as gradient boosting, decision tree, random forest, k-Nearest Neighbors (KNN), support vector machine (SVM), logistic regression, multi-layer perceptron (MLP), artificial neural network (ANN), and deep neural network (DNN) for multi-class classification of migraine. By employing advanced preprocessing techniques and publicly obtainable datasets, the study addresses the challenge of identifying different types of migraines that may share common variables. In this study, several machine learning (ML) models including gradient boosting, decision tree, random forest, k-Nearest Neighbors show that for multi-class migraine classification MLP and Gradient Boosting had good performance in most models, but did perform poorly in complex subcategories like Typical Aura with Migraine. Both attained high accuracies (96.4% and 97%, respectively). KNN and Logistic Regression, two traditional models, performed well at basic classifications but poorly at more complex situations; Neural networks (ANN and DNN) showed much flexibility towards data complexities. These results underscore how important it is to align model selection with data properties and provide avenues for improving performance through regularization and feature engineering. This strategy illustrates how AI-powered solutions can revolutionize the way we manage, treat, and prevent migraines across the globe.

Chokri Baccouch and Chaima Bahar, “Advanced Machine Learning Approaches for Accurate Migraine Prediction and Classification” International Journal of Advanced Computer Science and Applications(IJACSA), 16(1), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160101

@article{Baccouch2025,
title = {Advanced Machine Learning Approaches for Accurate Migraine Prediction and Classification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160101},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160101},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {1},
author = {Chokri Baccouch and Chaima Bahar}
}