Breast Cancer Detection Using Ensemble Classifiers for Accuracy Improvement
Early diagnosis of breast cancer plays a crucial role in treating the patient. Nowadays, data mining algorithms can provide intelligent methods in the health and treatment system that accurately detect breast cancer. The purpose of this study is breast cancer detection using ensemble classifier base...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | fas |
| Published: |
University of Qom
2023-03-01
|
| Series: | مدیریت مهندسی و رایانش نرم |
| Subjects: | |
| Online Access: | https://jemsc.qom.ac.ir/article_1596_fe5b6f445a70d95be07c9d4004656f17.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832577594232406016 |
|---|---|
| author | Mahboubeh Shamsi Mohadaseh Karimian Marziyeh Karimian |
| author_facet | Mahboubeh Shamsi Mohadaseh Karimian Marziyeh Karimian |
| author_sort | Mahboubeh Shamsi |
| collection | DOAJ |
| description | Early diagnosis of breast cancer plays a crucial role in treating the patient. Nowadays, data mining algorithms can provide intelligent methods in the health and treatment system that accurately detect breast cancer. The purpose of this study is breast cancer detection using ensemble classifier based on WBC and WDBC prepared databasesa. Our proposed model in the WBC database (reducing features by cfs+ optimizing samples using Resample+ ensemble classifier using data mining algorithms (kstar + random forest + Naïve Bayes and Bayes network)) has the best detection accuracy ( 100%), implementation time (0 seconds) and without any errors and on the WDBC database (reducing features by cfs+ optimizing samples using Resample+ ensemble classifier using data mining algorithms (IBK algorithm+ Naïve Bayes, Bayes network and kstar)) has an accuracy of 99/29, the implementation time is 0 seconds, and the mean absolute error is 0/007. The results of this study show that according to the ensemble classifier methods using data mining algorithms on the prepared database, new systems can be designed to help physicians that facilitate treatment processes. |
| format | Article |
| id | doaj-art-f96f28ffeca2427ab7e6e3d9428520eb |
| institution | Kabale University |
| issn | 2538-6239 2538-2675 |
| language | fas |
| publishDate | 2023-03-01 |
| publisher | University of Qom |
| record_format | Article |
| series | مدیریت مهندسی و رایانش نرم |
| spelling | doaj-art-f96f28ffeca2427ab7e6e3d9428520eb2025-01-30T20:18:25ZfasUniversity of Qomمدیریت مهندسی و رایانش نرم2538-62392538-26752023-03-0182921091596Breast Cancer Detection Using Ensemble Classifiers for Accuracy ImprovementMahboubeh Shamsi0Mohadaseh Karimian1Marziyeh Karimian2Assistant Prof. faculty of Electrical and Computer, Qom University of Technology, Qom, Irany. Email: shamsi@qut.ac.irMsc. of Computer Engineering, Faculty of Electrical and Computer Engineering, Shahab Danesh University, Qom, Iran. Email: m.karimian90@gmail.comMsc. of Computer Engineering, Faculty of Electrical and Computer Engineering, Shahab Danesh University, Qom, Iran. Email: m.karimian64@gmail.comEarly diagnosis of breast cancer plays a crucial role in treating the patient. Nowadays, data mining algorithms can provide intelligent methods in the health and treatment system that accurately detect breast cancer. The purpose of this study is breast cancer detection using ensemble classifier based on WBC and WDBC prepared databasesa. Our proposed model in the WBC database (reducing features by cfs+ optimizing samples using Resample+ ensemble classifier using data mining algorithms (kstar + random forest + Naïve Bayes and Bayes network)) has the best detection accuracy ( 100%), implementation time (0 seconds) and without any errors and on the WDBC database (reducing features by cfs+ optimizing samples using Resample+ ensemble classifier using data mining algorithms (IBK algorithm+ Naïve Bayes, Bayes network and kstar)) has an accuracy of 99/29, the implementation time is 0 seconds, and the mean absolute error is 0/007. The results of this study show that according to the ensemble classifier methods using data mining algorithms on the prepared database, new systems can be designed to help physicians that facilitate treatment processes.https://jemsc.qom.ac.ir/article_1596_fe5b6f445a70d95be07c9d4004656f17.pdfaccuracy improvementdata miningensemble classifiersfeature selectionsampling |
| spellingShingle | Mahboubeh Shamsi Mohadaseh Karimian Marziyeh Karimian Breast Cancer Detection Using Ensemble Classifiers for Accuracy Improvement مدیریت مهندسی و رایانش نرم accuracy improvement data mining ensemble classifiers feature selection sampling |
| title | Breast Cancer Detection Using Ensemble Classifiers for Accuracy Improvement |
| title_full | Breast Cancer Detection Using Ensemble Classifiers for Accuracy Improvement |
| title_fullStr | Breast Cancer Detection Using Ensemble Classifiers for Accuracy Improvement |
| title_full_unstemmed | Breast Cancer Detection Using Ensemble Classifiers for Accuracy Improvement |
| title_short | Breast Cancer Detection Using Ensemble Classifiers for Accuracy Improvement |
| title_sort | breast cancer detection using ensemble classifiers for accuracy improvement |
| topic | accuracy improvement data mining ensemble classifiers feature selection sampling |
| url | https://jemsc.qom.ac.ir/article_1596_fe5b6f445a70d95be07c9d4004656f17.pdf |
| work_keys_str_mv | AT mahboubehshamsi breastcancerdetectionusingensembleclassifiersforaccuracyimprovement AT mohadasehkarimian breastcancerdetectionusingensembleclassifiersforaccuracyimprovement AT marziyehkarimian breastcancerdetectionusingensembleclassifiersforaccuracyimprovement |