Deciphering the Symptom Spectrum: A Comprehensive Analysis of Migraine Patterns and Types

Abstract

Complex neurological diseases like migraine migraine affect a large section of the global population, causing health, social, and economic issues. Migraine causes intense, painful headaches that are usually one-sided and pulsing. Auras, nausea, vomiting, and excessive light and sound sensitivity may precede these episodes. Migraine affect millions worldwide and can be intermittent or persistent, impairing function. Diet and stress may induce it, but the cause is unknown. Prevention and symptom treatment drugs and lifestyle changes are used. Debilitating migraines are hard to diagnose due to their varied presentation and subjective symptom reporting. Traditional migraine diagnosis, based on clinical evaluation, typically fails to classify migraine types, requiring more objective and rigorous instruments. This study proposes a machine learning-based migraine categorization method to address this issue. The dataset includes different patient demographics and clinical variables; thus, we use complex algorithms like Random for Forest, XGBoost, and Extra Trees. These algorithms are great for deciphering migraine patterns because they excel at evaluating complex datasets. The research seeks to close this gap to improve migraine classification accuracy, objectivity, and reliability, enabling tailored migraine management and treatment. This neurology study could im- prove migraine diagnosis and treatment with more effective and personalized plans.