Migraine Triggers, Phases, and Classification with Machine Learning Models

Anusha Reddy ¹ Ajit Reddy ^2*

• ¹ San Juan Bautista School of Medicine, Caguas, Puerto Rico
• ² Lockheed Martin (United States), Bethesda, Maryland, United States

In many countries, patients with headache disorders such as migraine are among the underrecognized and under-diagnosed. Patients who are affected by headache disorders are not generally aware of the seriousness of these headaches since it does not cause death and are not contagious.In many cases, patients with migraine are often misdiagnosed as regular headaches.In this paper, we present the study of migraine, known triggers, different phases, classification of migraine into different types based on clinical studies, and the use of various machine learning algorithms such as logistic regression (LR), support vector machine (SVM), random forest (RF) and artificial neural network (ANN) to learn and classify different migraine types. This paper will only consider using these methods for diagnostic purposes. Models based on these algorithms are then trained using the dataset, a compilation of types of migraine experienced by various patients.These models are then used to classify the types of migraines, and the results are analyzed.The results of the machine learning models trained on the dataset are verified for their performance.The results are further evaluated by selective sampling and tuning, and improved performance is observed. The precision and accuracy obtained by the Support Vector Machine and Artificial Neural Network are 91% compared to Logistic Regression (90%) and Random Forest (87%).These models are run with the dataset without optimal tuning for the overall dataset for different migraine types, which is further improved with selective sampling and optimal tuning. These results indicate that the discussed models are relatively good and can be used with high precision and accuracy for diagnosing different types of migraine.Our study presents our realistic assessment of promising models that are dependable in aiding physicians. The study shows the performance of various models based on the classification metrics computed for each model. It is evident from the results that Artificial Neural Network (ANN) performs better, irrespective of the sampling techniques used. With these machine learning models, types of migraines can be classified with high accuracy and reliability so that physicians can make timely clinical diagnoses of patients.