Enhancing Migraine Classification Through Machine Learning: A Comparative Study of Ensemble Methods

A migraine is a common and complex neurological disorder affecting more than 90% of people globally. Traditional migraine diagnostic and classification methods are time-intensive and prone to error. In today’s world, where health and technology are closely connected, there is an urgent need for more advanced tools to accurately predict and classify migraine types. Machine learning (ML) has shown promise in automating migraine diagnoses and classification. However, individual ML classifiers may not always work well, which means that they need to be improved. In this paper, we used three ML classifiers that include decision tree, naïve Bayes, and k-nearest neighbor to classify seven different types of migraines. We also investigated ensemble classifiers like bagging, boosting, stacking, and majority voting to obtain better results. All classifiers were trained on a migraine dataset of 400 patients with 24 features. Before training the classifiers, we pre-processed the data by balancing the classes, removing useless features, and checking for correlations. After evaluating the performance, the results showed that majority voting achieved the highest accuracy improvement (7.59%), followed by boosting (6.55%), bagging (5.86%), and stacking (5.52%). These results indicate that the ensemble methods are effective in improving the classification accuracy of individual ML classifiers when it comes to classifying migraines.