Predicting Migraine Episodes with Deep Learning: Enabling Patients to Anticipate and Prevent Migraine Onset.

Migraine is a debilitating neurological condition that has shown a significant rise in prevalence globally, particularly most common among young to middle-aged females. Due to its profound impact on productivity, migraine has become a leading cause of missed deadlines and reduced efficiency, adversely affecting the education and work sectors. By 2024, numerous attempts have been made to treat and prevent migraines; however, its complex nature, particularly in terms of data variability and individual triggers, has limited the development of definitive solutions for prevention or cure. This research aims to develop a migraine prediction model using artificial intelligence (AI) by leveraging deep learning techniques capable of processing high-dimensional data. By analyzing monthly and weekly e-diary data from patients, we sought to uncover hidden patterns and key triggers contributing to migraine episodes. SHAP (SHapley Additive exPlanations) analysis was employed to identify the most impactful features serving as predictors for migraines, providing crucial insights into the condition's triggers. Subsequently, we evaluated various deep learning models to determine their suitability for handling high-dimensional data. Among the models tested, BiLSTM demonstrated the best performance, achieving a recall of 0.6805, compared to the Transformer's recall of 0.5844. Our findings underscore the significant potential of deep learning models, particularly BiLSTM, in aiding the medical field to analyze complex datasets. These models not only enable timely interventions but also pave the way for personalized healthcare solutions tailored to individual needs. This research highlights how AI-driven approaches can transform the understanding and management of complex chronic diseases like migraines, ultimately contributing to better patient outcomes.