Classification of Migraine Disease using Supervised Machine Learning

• Published in: 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) pp. 1 - 7
• Main Authors: Gulati, Seema, Guleria, Kalpna, Goyal, Nitin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 13.10.2022
• Subjects: Classification; Classification algorithms; J48 (Decision Tree); Logistic Regression; Machine Learning; Machine learning algorithms; Migraine Disease; Naive Bayes; Prediction algorithms; Random Forest; Random forests; SMO (SVM); Support vector machines; Task analysis
• DOI: 10.1109/ICRITO56286.2022.9964524

The study by Jama Network reveals that around 10% of the world is suffering from Migraine, which is a grievous disease of the brain and causes severe disability to the health of an individual. The pain in the head is so intense that the sufferers are unable to carry out their routine tasks once the migraine triggers. Therefore, it is considered the seventh most disease which can cause severe disability. Therefore, it is not only crucial to detect it at an early stage but also to identify the type of migraine for the sufferers to be treated timely. Machine learning has various applications related to the medical domain which supports the early prediction of disease. In this paper, five supervised machine learning methods have been used to classify the disease based on the symptoms of the subjects. The best-suited algorithm is found for the classification task and the implementation of the algorithms is completed using the Weka data mining tool. The results show that the best-suited algorithm out of the chosen popular models for the classification task is the simplistic model Naive Bayes.