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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| Format: | Article |
| Language: | English |
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Institute of Advanced Engineering and Science
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/34620/ http://umpir.ump.edu.my/id/eprint/34620/1/Breast%20cancer%20disease%20classification%20using%20fuzzy-ID3%20algorithm%20based%20on%20association%20function.pdf |
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| author | Nur Farahaina, Idris Mohd Arfian, Ismail Mohd Saberi, Mohamad Shahreen, Kasim Zalmiyah, Zakaria Sutikno, Tole |
| author_facet | Nur Farahaina, Idris Mohd Arfian, Ismail Mohd Saberi, Mohamad Shahreen, Kasim Zalmiyah, Zakaria Sutikno, Tole |
| author_sort | Nur Farahaina, Idris |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T03:14:53Z |
| format | Article |
| id | ump-34620 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:14:53Z |
| publishDate | 2022 |
| publisher | Institute of Advanced Engineering and Science |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-346202022-11-09T07:09:46Z http://umpir.ump.edu.my/id/eprint/34620/ Breast cancer disease classification using fuzzy-ID3 algorithm based on association function Nur Farahaina, Idris Mohd Arfian, Ismail Mohd Saberi, Mohamad Shahreen, Kasim Zalmiyah, Zakaria Sutikno, Tole QA75 Electronic computers. Computer science QA76 Computer software RC0254 Neoplasms. Tumors. Oncology (including Cancer) TA Engineering (General). Civil engineering (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 2022-06 Article PeerReviewed pdf en cc_by_sa_4 http://umpir.ump.edu.my/id/eprint/34620/1/Breast%20cancer%20disease%20classification%20using%20fuzzy-ID3%20algorithm%20based%20on%20association%20function.pdf Nur Farahaina, Idris and Mohd Arfian, Ismail and Mohd Saberi, Mohamad and Shahreen, Kasim and Zalmiyah, Zakaria 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. (Published) https://doi.org/ 10.11591/ijai.v11.i2.pp448-461 https://doi.org/ 10.11591/ijai.v11.i2.pp448-461 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software RC0254 Neoplasms. Tumors. Oncology (including Cancer) TA Engineering (General). Civil engineering (General) Nur Farahaina, Idris Mohd Arfian, Ismail Mohd Saberi, Mohamad Shahreen, Kasim Zalmiyah, Zakaria Sutikno, Tole Breast cancer disease classification using fuzzy-ID3 algorithm based on association function |
| title | 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_short | 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 |
| topic | QA75 Electronic computers. Computer science QA76 Computer software RC0254 Neoplasms. Tumors. Oncology (including Cancer) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/34620/ http://umpir.ump.edu.my/id/eprint/34620/ http://umpir.ump.edu.my/id/eprint/34620/ http://umpir.ump.edu.my/id/eprint/34620/1/Breast%20cancer%20disease%20classification%20using%20fuzzy-ID3%20algorithm%20based%20on%20association%20function.pdf |