Klasifikasi Penyakit Migrain dengan Metode Naïve Bayes pada Dataset Kaggle
Original Full-Text Article
Download published version for reading and archivingAbstract
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.
Keywords:
Author Biographies
Program Studi Teknik Informatika, Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana, Kota Salatiga, Provinsi Jawa Tengah, Indonesia.
Program Studi Teknik Informatika, Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana, Kota Salatiga, Provinsi Jawa Tengah, Indonesia.
How to Cite
This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License .
- Share: You are free to copy, distribute, and transmit the work in any medium or format.
- Adapt: You are free to remix, transform, and build upon the work for any purpose, even commercially.
- Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
References
Total: 15 References- Abyuda, K. P. P., & Kurniawan, S. N. (2021). Complicated migraine. Journal of Pain, Headache and Vertigo, 2(2), 28-33.
- Bianto, M. A., Kusrini, K., & Sudarmawan, S. (2020). Perancangan Sistem Klasifikasi Penyakit Jantung Mengunakan Naïve Bayes. Creative Information Technology Journal, 6(1), 75-83. DOI: https://doi.org/10.24076/citec.2019v6i1.231.
- Eigenbrodt, A. K., Ashina, H., Khan, S., Diener, H. C., Mitsikostas, D. D., Sinclair, A. J., ... & Ashina, M. (2021). Diagnosis and management of migraine in ten steps. Nature Reviews Neurology, 17(8), 501-514. DOI: https://doi.org/10.1038/s41582-021-00509-5.
- Fadri, W. (2023). Klasifikasi Penyakit Hati dengan Menggunakan Metode Naive Bayes. Jurnal Informasi Dan Teknologi, 32-37. DOI: https://doi.org/10.37034/jidt.v5i1.230.
- Iskandar, J. W., & Nataliani, Y. (2021). Perbandingan Naïve Bayes, SVM, dan k-NN untuk Analisis Sentimen Gadget Berbasis Aspek. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 5(6), 1120-1126. DOI: https://doi.org/10.29207/resti.v5i6.3588.
Similar Articles
Articles sharing related keywords and machine learning classifications:
Most read articles by the same author(s)
Other papers published by author(s) in this journal: