Browsing by Author "Adnan, Muhammad"

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    Exploring the Prevalence and Triggering Factors of Migraine in University Students of Bangladesh Using Machine Learning
    (Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2023-05-30) Saif, Zawwad Bin; Sakib, Nazmus; Adnan, Muhammad
    Migraine is a recurring neurovascular illness causing prolonged acute pain, nausea, vomiting, and autonomic nervous system dysfunction, resulting from disrupted blood vessels and nerve signals in the brain due to unbalanced activity, whose exact cause remains unknown influencing significantly the quality of life. The study aims to explore the prevalence of migraine among Bangladesh's university students, predict their occurrence based on triggering factors using machine learning, and raise awareness to facilitate the everyday activities of migraine patients. Around 303 students from various universities in Bangladesh participated in this cross-sectional survey. in an interval between August to October of 2022 via means of a voluntarily completed online platform-based questionnaire. For the survey structure, a total of twenty factors were sorted out after keen observation that triggers the migraine and subsequently, a dataset was structured based on the factors. The prevalence of migraine and these 20 triggering factors of migraine among university students were determined through this survey. To generate a probabilistic prediction of the occurrence of migraine, nine ML algorithms have been applied for male and female participants separately considering the headache-triggering factors. With some data preprocessing and feature engineering, GridSearchCV was used to optimize the hyperparameters for each of the nine classification models to achieve more efficient results. ML algorithms are compared by examining their several performance matrices like accuracy, train score, precision, recall, F1 score, and ROC-AUC value and after extensive simulation, the Logistic Regression algorithm emerged with the highest accuracy of 78.1% for the male participants. The stacking Classifier and Random Forest Classifier emerged with the highest accuracy of 85.3% in the case of the female participants. Making use of various machine learning algorithms and clinical data in this field has the potential to make it simpler for people with migraines to identify and avoid the triggers of their condition, allowing them to go about their daily lives more comfortably