Cerebral Infarction Classification Using the K-Nearest Neighbor and Naive Bayes Classifier

S. H. Rukmawan, F. R. Aszhari, Z. Rustam, J. Pandelaki

Research output: Contribution to journalConference articlepeer-review

8 Citations (Scopus)


Cerebral infarction is one of the causes of stroke in the brain and is included in ischemic stroke. To detect infarction in the brain, classification in machine learning can be used. They are k-Nearest Neighbor (kNN) and Naive Bayes (NB). kNN is a simple and well-known machine learning method with high accuracy values. however, kNN can produce sub-optimal results if very little training data is used. Because it will produce accuracy from a biased model and has less than optimal performance. Meanwhile, Naive Bayes Classifier has a better level of accuracy compared to other classifier models. And only requires a small training test to get high accuracy. Therefore, this study will compare 2 different classifications to get the highest accuracy in the brain infarction dataset obtained from the Department of Radiology, dr. Cipto Mangunkusumo Hospital (RSCM). The accuracy of this method reaches 91% for kNN and 97% for Naive Bayes.

Original languageEnglish
Article number012045
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 15 Feb 2021
Event3rd International Conference on Statistics, Mathematics, Teaching, and Research 2019, ICSMTR 2019 - Makassar, Indonesia
Duration: 9 Oct 201910 Oct 2019


  • Classsification
  • K-nearset neighbor
  • naive bayes classifier


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