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Breast cancer disease classification using fuzzy-id3 algorithm based on association function

Breast cancer is the second leading cause of mortality among female cancer patients worldwide. Early detection of breast cancer is considerd as one of the most effective ways to prevent the disease from spreading and enable human can make correct decision on the next process. Automatic diagnostic me...

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Main Authors: Idris, Nur Farahaina, Ismail, Mohd. Arfian, Mohamad, Mohd. Saberi, Kasim, Shahreen, Zakaria, Zalmiyah, Sutikno, Tole
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science 2022
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/id/eprint/98666/1/ZalmiyahZakaria2022_BreastCancerDiseaseClassification.pdf
http://eprints.utm.my/id/eprint/98666/
http://dx.doi.org/10.11591/ijai.v11.i2.pp448-461
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spelling my.utm.986662023-01-30T04:37:49Z http://eprints.utm.my/id/eprint/98666/ Breast cancer disease classification using fuzzy-id3 algorithm based on association function Idris, Nur Farahaina Ismail, Mohd. Arfian Mohamad, Mohd. Saberi Kasim, Shahreen Zakaria, Zalmiyah Sutikno, Tole T Technology (General) Breast cancer is the second leading cause of mortality among female cancer patients worldwide. Early detection of breast cancer is considerd as one of the most effective ways to prevent the disease from spreading and enable human can make correct decision on the next process. Automatic diagnostic methods were frequently used to conduct breast cancer diagnoses in order to increase the accuracy and speed of detection. The fuzzy-ID3 algorithm with association function implementation (FID3-AF) is proposed as a classification technique for breast cancer detection. The FID3-AF algorithm is a hybridisation of the fuzzy system, the iterative dichotomizer 3 (ID3) algorithm, and the association function. The fuzzy-neural dynamic-bottleneck-detection (FUZZYDBD) is considered as an automatic fuzzy database definition method, would aid in the development of the fuzzy database for the data fuzzification process in FID3-AF. The FID3-AF overcame ID3’s issue of being unable to handle continuous data. The association function is implemented to minimise overfitting and enhance generalisation ability. The results indicated that FID3-AF is robust in breast cancer classification. A thorough comparison of FID3-AF to numerous existing methods was conducted to validate the proposed method’s competency. This study established that the FID3-AF performed well and outperform other methods in breast cancer classification. Institute of Advanced Engineering and Science 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/98666/1/ZalmiyahZakaria2022_BreastCancerDiseaseClassification.pdf Idris, Nur Farahaina and Ismail, Mohd. Arfian and Mohamad, Mohd. Saberi and Kasim, Shahreen and Zakaria, Zalmiyah and Sutikno, Tole (2022) Breast cancer disease classification using fuzzy-id3 algorithm based on association function. IAES International Journal of Artificial Intelligence, 11 (2). pp. 448-461. ISSN 2089-4872 http://dx.doi.org/10.11591/ijai.v11.i2.pp448-461 DOI: 10.11591/ijai.v11.i2.pp448-461
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Idris, Nur Farahaina
Ismail, Mohd. Arfian
Mohamad, Mohd. Saberi
Kasim, Shahreen
Zakaria, Zalmiyah
Sutikno, Tole
Breast cancer disease classification using fuzzy-id3 algorithm based on association function
description Breast cancer is the second leading cause of mortality among female cancer patients worldwide. Early detection of breast cancer is considerd as one of the most effective ways to prevent the disease from spreading and enable human can make correct decision on the next process. Automatic diagnostic methods were frequently used to conduct breast cancer diagnoses in order to increase the accuracy and speed of detection. The fuzzy-ID3 algorithm with association function implementation (FID3-AF) is proposed as a classification technique for breast cancer detection. The FID3-AF algorithm is a hybridisation of the fuzzy system, the iterative dichotomizer 3 (ID3) algorithm, and the association function. The fuzzy-neural dynamic-bottleneck-detection (FUZZYDBD) is considered as an automatic fuzzy database definition method, would aid in the development of the fuzzy database for the data fuzzification process in FID3-AF. The FID3-AF overcame ID3’s issue of being unable to handle continuous data. The association function is implemented to minimise overfitting and enhance generalisation ability. The results indicated that FID3-AF is robust in breast cancer classification. A thorough comparison of FID3-AF to numerous existing methods was conducted to validate the proposed method’s competency. This study established that the FID3-AF performed well and outperform other methods in breast cancer classification.
format Article
author Idris, Nur Farahaina
Ismail, Mohd. Arfian
Mohamad, Mohd. Saberi
Kasim, Shahreen
Zakaria, Zalmiyah
Sutikno, Tole
author_facet Idris, Nur Farahaina
Ismail, Mohd. Arfian
Mohamad, Mohd. Saberi
Kasim, Shahreen
Zakaria, Zalmiyah
Sutikno, Tole
author_sort Idris, Nur Farahaina
title Breast cancer disease classification using fuzzy-id3 algorithm based on association function
title_short Breast cancer disease classification using fuzzy-id3 algorithm based on association function
title_full Breast cancer disease classification using fuzzy-id3 algorithm based on association function
title_fullStr Breast cancer disease classification using fuzzy-id3 algorithm based on association function
title_full_unstemmed Breast cancer disease classification using fuzzy-id3 algorithm based on association function
title_sort breast cancer disease classification using fuzzy-id3 algorithm based on association function
publisher Institute of Advanced Engineering and Science
publishDate 2022
url http://eprints.utm.my/id/eprint/98666/1/ZalmiyahZakaria2022_BreastCancerDiseaseClassification.pdf
http://eprints.utm.my/id/eprint/98666/
http://dx.doi.org/10.11591/ijai.v11.i2.pp448-461
_version_ 1756684240959832064
score 13.211869

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