Expert systems for self-diagnosing of eye diseases using Naïve Bayes

Rahmad Kurniawan, Novi Yanti, Mohd Zakree Ahmad Nazri, Zulvandri

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

5 Citations (Scopus)

Abstract

The best defense against eye diseases is to have regular checkups. However, in reality, poverty stops people outside the developing world from seeing an eye doctor regularly. Thus, many patients did not get appropriate treatment for their eye disease until it is too late. This paper presents an expert system for diagnosing eye disease based on Naive Bayes. The developed expert system applies Case-Based Reasoning (CBR), which is a paradigm for reasoning from experience while the Naïve Bayes is used as a method for classifying eye diseases by applying Bayes' theorem. The outputs of the expert system are classification of an eye disease and information on the best treatment. The result of this study is obtained by comparing the expert system diagnostic results with an expert diagnostic result. Based on the experimental results, the Naïve Bayes based expert system has been able to obtained 82% accuracy. Thus, it can be concluded that an expert system with Naïve Bayes has the potential to be used effectively by the people but still has plenty room for improvement.

Original languageEnglish
Title of host publicationProceedings - 2014 International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages113-116
Number of pages4
ISBN (Print)9781479951000
DOIs
Publication statusPublished - 9 Jan 2015
Event2014 International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2014 - Bandung, Indonesia
Duration: 20 Aug 201421 Aug 2014

Other

Other2014 International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2014
CountryIndonesia
CityBandung
Period20/8/1421/8/14

Fingerprint

Expert systems
Case based reasoning

Keywords

  • Case-Based Reasoning
  • Expert System
  • Eye Disease
  • Naïve Bayes

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Science Applications

Cite this

Kurniawan, R., Yanti, N., Ahmad Nazri, M. Z., & Zulvandri (2015). Expert systems for self-diagnosing of eye diseases using Naïve Bayes. In Proceedings - 2014 International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2014 (pp. 113-116). [7005925] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAICTA.2014.7005925

Expert systems for self-diagnosing of eye diseases using Naïve Bayes. / Kurniawan, Rahmad; Yanti, Novi; Ahmad Nazri, Mohd Zakree; Zulvandri.

Proceedings - 2014 International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2014. Institute of Electrical and Electronics Engineers Inc., 2015. p. 113-116 7005925.

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

Kurniawan, R, Yanti, N, Ahmad Nazri, MZ & Zulvandri 2015, Expert systems for self-diagnosing of eye diseases using Naïve Bayes. in Proceedings - 2014 International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2014., 7005925, Institute of Electrical and Electronics Engineers Inc., pp. 113-116, 2014 International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2014, Bandung, Indonesia, 20/8/14. https://doi.org/10.1109/ICAICTA.2014.7005925
Kurniawan R, Yanti N, Ahmad Nazri MZ, Zulvandri. Expert systems for self-diagnosing of eye diseases using Naïve Bayes. In Proceedings - 2014 International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2014. Institute of Electrical and Electronics Engineers Inc. 2015. p. 113-116. 7005925 https://doi.org/10.1109/ICAICTA.2014.7005925
Kurniawan, Rahmad ; Yanti, Novi ; Ahmad Nazri, Mohd Zakree ; Zulvandri. / Expert systems for self-diagnosing of eye diseases using Naïve Bayes. Proceedings - 2014 International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2014. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 113-116
@inproceedings{0382c7d91c09463581e3b5f8795cbe65,
title = "Expert systems for self-diagnosing of eye diseases using Na{\"i}ve Bayes",
abstract = "The best defense against eye diseases is to have regular checkups. However, in reality, poverty stops people outside the developing world from seeing an eye doctor regularly. Thus, many patients did not get appropriate treatment for their eye disease until it is too late. This paper presents an expert system for diagnosing eye disease based on Naive Bayes. The developed expert system applies Case-Based Reasoning (CBR), which is a paradigm for reasoning from experience while the Na{\"i}ve Bayes is used as a method for classifying eye diseases by applying Bayes' theorem. The outputs of the expert system are classification of an eye disease and information on the best treatment. The result of this study is obtained by comparing the expert system diagnostic results with an expert diagnostic result. Based on the experimental results, the Na{\"i}ve Bayes based expert system has been able to obtained 82{\%} accuracy. Thus, it can be concluded that an expert system with Na{\"i}ve Bayes has the potential to be used effectively by the people but still has plenty room for improvement.",
keywords = "Case-Based Reasoning, Expert System, Eye Disease, Na{\"i}ve Bayes",
author = "Rahmad Kurniawan and Novi Yanti and {Ahmad Nazri}, {Mohd Zakree} and Zulvandri",
year = "2015",
month = "1",
day = "9",
doi = "10.1109/ICAICTA.2014.7005925",
language = "English",
isbn = "9781479951000",
pages = "113--116",
booktitle = "Proceedings - 2014 International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Expert systems for self-diagnosing of eye diseases using Naïve Bayes

AU - Kurniawan, Rahmad

AU - Yanti, Novi

AU - Ahmad Nazri, Mohd Zakree

AU - Zulvandri,

PY - 2015/1/9

Y1 - 2015/1/9

N2 - The best defense against eye diseases is to have regular checkups. However, in reality, poverty stops people outside the developing world from seeing an eye doctor regularly. Thus, many patients did not get appropriate treatment for their eye disease until it is too late. This paper presents an expert system for diagnosing eye disease based on Naive Bayes. The developed expert system applies Case-Based Reasoning (CBR), which is a paradigm for reasoning from experience while the Naïve Bayes is used as a method for classifying eye diseases by applying Bayes' theorem. The outputs of the expert system are classification of an eye disease and information on the best treatment. The result of this study is obtained by comparing the expert system diagnostic results with an expert diagnostic result. Based on the experimental results, the Naïve Bayes based expert system has been able to obtained 82% accuracy. Thus, it can be concluded that an expert system with Naïve Bayes has the potential to be used effectively by the people but still has plenty room for improvement.

AB - The best defense against eye diseases is to have regular checkups. However, in reality, poverty stops people outside the developing world from seeing an eye doctor regularly. Thus, many patients did not get appropriate treatment for their eye disease until it is too late. This paper presents an expert system for diagnosing eye disease based on Naive Bayes. The developed expert system applies Case-Based Reasoning (CBR), which is a paradigm for reasoning from experience while the Naïve Bayes is used as a method for classifying eye diseases by applying Bayes' theorem. The outputs of the expert system are classification of an eye disease and information on the best treatment. The result of this study is obtained by comparing the expert system diagnostic results with an expert diagnostic result. Based on the experimental results, the Naïve Bayes based expert system has been able to obtained 82% accuracy. Thus, it can be concluded that an expert system with Naïve Bayes has the potential to be used effectively by the people but still has plenty room for improvement.

KW - Case-Based Reasoning

KW - Expert System

KW - Eye Disease

KW - Naïve Bayes

UR - http://www.scopus.com/inward/record.url?scp=84961333427&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84961333427&partnerID=8YFLogxK

U2 - 10.1109/ICAICTA.2014.7005925

DO - 10.1109/ICAICTA.2014.7005925

M3 - Conference contribution

AN - SCOPUS:84961333427

SN - 9781479951000

SP - 113

EP - 116

BT - Proceedings - 2014 International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2014

PB - Institute of Electrical and Electronics Engineers Inc.

ER -