Reviewing the consistency of the Naïve Bayes Classifier's performance in medical diagnosis and prognosis problems

N. A. Fauziyyah, S. Abdullah, S. Nurrohmah

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Naïve Bayes Classifier (NBC) is one of the most popular machine learning methods that has been applied in various fields, some of them being text classification, medical diagnosis and systems performance management. Claims about the excellence of this classifier's performance has been stated in several literatures. The main goal of this study is to assess the consistency of this classifier's performance by applying it to five medical datasets and compare the result with another popular classification method, Decision Tree (DT). Results present some empirical proofs that NBC has a consistent performance, performs better compared to DT by outperforming it in four out of the five datasets that are used and is robust to the presence of missing value. This consistent performance may be due to the nature and background of the datasets used, in which datasets with the right attribute variables tend to produce a high true positive rate and accuracy score.

Original languageEnglish
Title of host publicationProceedings of the 5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019
EditorsTerry Mart, Djoko Triyono, Tribidasari Anggraningrum Ivandini
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735420014
DOIs
Publication statusPublished - 1 Jun 2020
Event5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019 - Depok, Indonesia
Duration: 9 Jul 201910 Jul 2019

Publication series

NameAIP Conference Proceedings
Volume2242
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019
CountryIndonesia
CityDepok
Period9/07/1910/07/19

Keywords

  • Bayes rule
  • breast cancer
  • classification
  • decision tree
  • prior class probability

Fingerprint Dive into the research topics of 'Reviewing the consistency of the Naïve Bayes Classifier's performance in medical diagnosis and prognosis problems'. Together they form a unique fingerprint.

  • Cite this

    Fauziyyah, N. A., Abdullah, S., & Nurrohmah, S. (2020). Reviewing the consistency of the Naïve Bayes Classifier's performance in medical diagnosis and prognosis problems. In T. Mart, D. Triyono, & T. A. Ivandini (Eds.), Proceedings of the 5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019 [030019] (AIP Conference Proceedings; Vol. 2242). American Institute of Physics Inc.. https://doi.org/10.1063/5.0007885