A New Knowledge-Based System for Diagnosis of Breast Cancer by a combination of the Affinity Propagation and Firefly Algorithms
Breast cancer has become a widespread disease around the world in young women. Expert systems, developed by data mining techniques, are valuable tools in diagnosis of breast cancer and can help physicians for decision making process. This paper presents a new hybrid data mining approach to classify...
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| Format: | Article |
| Language: | English |
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Shahrood University of Technology
2019-03-01
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| Series: | Journal of Artificial Intelligence and Data Mining |
| Subjects: | |
| Online Access: | http://jad.shahroodut.ac.ir/article_1264_d999525c12ca7d1e66e1d8e258c4854a.pdf |
| _version_ | 1818836745552134144 |
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| author | N. Emami A. Pakzad |
| author_facet | N. Emami A. Pakzad |
| author_sort | N. Emami |
| collection | DOAJ |
| description | Breast cancer has become a widespread disease around the world in young women. Expert systems, developed by data mining techniques, are valuable tools in diagnosis of breast cancer and can help physicians for decision making process. This paper presents a new hybrid data mining approach to classify two groups of breast cancer patients (malignant and benign). The proposed approach, AP-AMBFA, consists of two phases. In the first phase, the Affinity Propagation (AP) clustering method is used as instances reduction technique which can find noisy instance and eliminate them. In the second phase, feature selection and classification are conducted by the Adaptive Modified Binary Firefly Algorithm (AMBFA) for selection of the most related predictor variables to target variable and Support Vectors Machine (SVM) technique as classifier. It can reduce the computational complexity and speed up the data mining process. Experimental results on Wisconsin Diagnostic Breast Cancer (WDBC) datasets show higher predictive accuracy. The obtained classification accuracy is 98.606%, a very promising result compared to the current state-of-the-art classification techniques applied to the same database. Hence this method will help physicians in more accurate diagnosis of breast cancer. |
| first_indexed | 2024-12-19T03:11:29Z |
| format | Article |
| id | doaj.art-e6ce1bc760a749e9849a5b0692f91524 |
| institution | Directory Open Access Journal |
| issn | 2322-5211 2322-4444 |
| language | English |
| last_indexed | 2024-12-19T03:11:29Z |
| publishDate | 2019-03-01 |
| publisher | Shahrood University of Technology |
| record_format | Article |
| series | Journal of Artificial Intelligence and Data Mining |
| spelling | doaj.art-e6ce1bc760a749e9849a5b0692f915242022-12-21T20:38:00ZengShahrood University of TechnologyJournal of Artificial Intelligence and Data Mining2322-52112322-44442019-03-0171596810.22044/jadm.2018.6489.17631264A New Knowledge-Based System for Diagnosis of Breast Cancer by a combination of the Affinity Propagation and Firefly AlgorithmsN. Emami0A. Pakzad1Department of Computer Science, Faculty of Basic Sciences, Kosar University of Bojnord, Bojnord, IranDepartment of Industrial Engineering, Faculty of Engineering , Kosar University of Bojnord, Bojnord, Iran.Breast cancer has become a widespread disease around the world in young women. Expert systems, developed by data mining techniques, are valuable tools in diagnosis of breast cancer and can help physicians for decision making process. This paper presents a new hybrid data mining approach to classify two groups of breast cancer patients (malignant and benign). The proposed approach, AP-AMBFA, consists of two phases. In the first phase, the Affinity Propagation (AP) clustering method is used as instances reduction technique which can find noisy instance and eliminate them. In the second phase, feature selection and classification are conducted by the Adaptive Modified Binary Firefly Algorithm (AMBFA) for selection of the most related predictor variables to target variable and Support Vectors Machine (SVM) technique as classifier. It can reduce the computational complexity and speed up the data mining process. Experimental results on Wisconsin Diagnostic Breast Cancer (WDBC) datasets show higher predictive accuracy. The obtained classification accuracy is 98.606%, a very promising result compared to the current state-of-the-art classification techniques applied to the same database. Hence this method will help physicians in more accurate diagnosis of breast cancer.http://jad.shahroodut.ac.ir/article_1264_d999525c12ca7d1e66e1d8e258c4854a.pdfBreast CancerAffinity PropagationFeature SelectionBinary Firefly AlgorithmSupport Vectors Machine |
| spellingShingle | N. Emami A. Pakzad A New Knowledge-Based System for Diagnosis of Breast Cancer by a combination of the Affinity Propagation and Firefly Algorithms Journal of Artificial Intelligence and Data Mining Breast Cancer Affinity Propagation Feature Selection Binary Firefly Algorithm Support Vectors Machine |
| title | A New Knowledge-Based System for Diagnosis of Breast Cancer by a combination of the Affinity Propagation and Firefly Algorithms |
| title_full | A New Knowledge-Based System for Diagnosis of Breast Cancer by a combination of the Affinity Propagation and Firefly Algorithms |
| title_fullStr | A New Knowledge-Based System for Diagnosis of Breast Cancer by a combination of the Affinity Propagation and Firefly Algorithms |
| title_full_unstemmed | A New Knowledge-Based System for Diagnosis of Breast Cancer by a combination of the Affinity Propagation and Firefly Algorithms |
| title_short | A New Knowledge-Based System for Diagnosis of Breast Cancer by a combination of the Affinity Propagation and Firefly Algorithms |
| title_sort | new knowledge based system for diagnosis of breast cancer by a combination of the affinity propagation and firefly algorithms |
| topic | Breast Cancer Affinity Propagation Feature Selection Binary Firefly Algorithm Support Vectors Machine |
| url | http://jad.shahroodut.ac.ir/article_1264_d999525c12ca7d1e66e1d8e258c4854a.pdf |
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