Year: 2025
Type: conference-paper
Abstract: Migraine is a common neurological illness that has a major influence on the quality of life; yet, precise categorization and prediction remain difficult because of its complicated symptoms and multiple triggers. This work investigates the use of advanced machine learning (ML) algorithms to improve migraine diagnosis and prediction, drawing on a large dataset that includes clinical, lifestyle, and environmental aspects. Various machine learning models, such as ensemble methods, deep learning, and hybrid approaches, are tested to see how well they discriminate migraine from other headache conditions and predict migraine episodes. Feature selection approaches are used to identify the most important predictors, which improve model interpretability and performance. Experimental results show that the proposed machine learning framework outperforms established diagnostic methods in terms of classification accuracy, sensitivity, and specificity. The study demonstrates how ML-driven solutions may be used to manage migraines in a tailored way, helping medical practitioners with early diagnosis and intervention techniques. My suggested framework, NeuroVote(ensemble model), offers a remarkable 99.99% classification accuracy for migraines. Future studies will concentrate on optimizing models for clinical deployment and incorporating real-time data from wearable technology.
Authors: Irsa Imtiaz, Hamza Afzal, Attique Ur Rehman, Gina Purnama Insany
Institutions: University of Sialkot, Nusa Putra University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
OpenAlex ID: W4414989309