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Naïve Bayes Classifier Models for Predicting the Colon Cancer

IOP conference series. Materials Science and Engineering, 2019-06, Vol.546 (5), p.52068 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;2019. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1757-8981 ;EISSN: 1757-899X ;DOI: 10.1088/1757-899X/546/5/052068

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  • Title:
    Naïve Bayes Classifier Models for Predicting the Colon Cancer
  • Author: Salmi, Nafizatus ; Rustam, Zuherman
  • Subjects: Accuracy ; Bayes Theorem ; Classification ; Classifiers ; Colon ; Colorectal cancer ; Genes ; Machine learning ; Model accuracy
  • Is Part Of: IOP conference series. Materials Science and Engineering, 2019-06, Vol.546 (5), p.52068
  • Description: Cancer has been known as a disease consisting of several different types. Cancer is a life threatening disease in the world today. There are so many types of cancer in the world, one of which is colon cancer. Colon cancer is one of the number one killers in the world. However, because there isn't any obvious symptom of colon cancer at an early stage, people do not realize that they suffer from it. Even though cancer formation is different for each type of cancer, it is still a big challenge to make cancer classification with good accuracy. Many machine learning has been applied to the data of human's genes in order to get the most relevant genes in the classification of cancer. The author proposes the Naïve Bayes Classifier model as a classification method to show that the model has good accuracy, good precision, good recall, good f1 - score in classifying the data of patients suffering from colon cancer or not. In this proposed model, Naïve Bayes Classifier is a technique prediction based on simple probabilistic and on the application of the Bayes theorem (or Bayes rule) with a strong independence assumption. Therefore, this model is able to make higher classification accuracy with less complexity. In particular, it achieves up to 95.24% classification accuracy, thus this model can be an efficient analysis tool.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1757-8981
    EISSN: 1757-899X
    DOI: 10.1088/1757-899X/546/5/052068
  • Source: Geneva Foundation Free Medical Journals at publisher websites
    IOPscience (Open Access)
    IOP Publishing Free Content(OpenAccess)
    ProQuest Central
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