The Convergence of Ensemble and Deep Learning Paradigms in Migraine Classification: A Comparative Methodologies Synthesis

Author(s)	Ms. Nikita Pravin Bichitkar
Country	India
Abstract	Migraine is a debilitating neurological disorder with a complex, heterogeneous pathophysiology that complicates clinical diagnosis and subtype differentiation. Traditional diagnostic frameworks, such as the ICHD-3, often rely on subjective patient reporting, leading to potential misclassification and delayed treatment. This paper provides a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) architectures- including Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting (XGBoost), and Convolution Neural Networks (CNN)- applied to migraine classification. We evaluate models based on diverse data modalities, including clinical questionnaires, neuroimaging (fMRI/MRI), and electrophysiological Signals (EEG). This review synthesizes 25 seminal papers, highlighting that ensemble methods and DL models consistently outperform standalone classifiers, with accuracies exceeding 95%. However, Challenges such as dataset imbalance and model interpretability (the “black box” problem) remain. We conclude with a roadmap for integrating Explainable AI (XAI) into clinical decision support systems.
Keywords	Migraine Classification, Machine Learning, Deep Learning, Clinical Decision Support, Neuroimaging, EEG, Ensemble Learning, Explainable AI.
Field	Computer
Published In	Volume 17, Issue 2, April-June 2026
Published On	2026-06-06

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