Improving Automatic Migraine Classification Performance with Naive Bayes - Arzum Karataş (Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi)
The expertise of the physician and the patient's ongoing observation are the two primary contributing factors in diagnosing migraines. However, individuals who experience migraines in the early stages frequently visit emergency rooms or different outpatient clinics, such as internal medicine, ophthalmology, and family medicine. Additionally, the type of migraine is frequently misdiagnosed due to the severity of the symptoms being misjudged or because the five-to-ten-minute examination period is insufficient for achieving an accurate diagnosis. Incorrect treatment of this type can have adverse effects on the patient's health. The majority of research in this field has concentrated on the study of brainwaves, leading to the development of complex tests that are only available to a small proportion of the population. However, one study has made progress in automatic migraine classification. The study, which demonstrates 97% classification performance above that of previous studies and produces findings in a timely manner, provides a decision support mechanism that will assist clinicians in the proper classification of migraine type. Given that over 20% of Turkey's population suffers from migraines, our study concentrated on the same issue to enhance classification performance in terms of accuracy and training time. The Naive Bayes model was employed in the study to categorize the various types of migraines, and the performance of the model was evaluated using data from actual migraine sufferers. The classification model utilized exhibited superior classification performance compared to previous studies, with 99% accuracy and precision. Additionally, the model's training time in the same dataset was the shortest when compared to other benchmarked classifier models. The application of the Naive Bayes classifier to the classification of migraines represents a highly effective technique that can facilitate rapid, accurate clinical diagnoses, thereby enabling physicians to provide their patients with precise diagnoses.