Klasifikasi Penyakit Migrain dengan Metode Naïve Bayes pada Dataset Kaggle

Abstract

Migraine is one of the most common neurological diseases in society and has a significant disability impact. According to the Global Burden of Disease Study, migraine is one of the most common neurological disorders in the world, with a greater disability burden than other neurological disorders. In data science, data classification plays an important role in determining the category or class of an object based on a number of available classes. One frequently used classification method is Naïve Bayes, which utilizes mathematical probabilities with the assumption that the decision made is accurate based on the given data. This research develops a classification model using the Naïve Bayes algorithm to predict and classify migraine patient data. This model produces classification values with an accuracy of 88.51% when training from 280 train data given and 89.02% when testing from 120 test data given. The classification results can support the medical world in diagnosing migraine types more accurately, optimizing treatment, saving health costs, and becoming the basis for further research and development in the field of neurology.