Breast Tumor Classification Using an Ensemble Machine Learning Method
Breast cancer is the most common cause of death for women worldwide. Thus, the ability of artificial intelligence systems to detect possible breast cancer is very important. In this paper, an ensemble classification mechanism is proposed based on a majority voting mechanism. First, the performance o...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-05-01
|
| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/6/6/39 |
| _version_ | 1797566725069209600 |
|---|---|
| author | Adel S. Assiri Saima Nazir Sergio A. Velastin |
| author_facet | Adel S. Assiri Saima Nazir Sergio A. Velastin |
| author_sort | Adel S. Assiri |
| collection | DOAJ |
| description | Breast cancer is the most common cause of death for women worldwide. Thus, the ability of artificial intelligence systems to detect possible breast cancer is very important. In this paper, an ensemble classification mechanism is proposed based on a majority voting mechanism. First, the performance of different state-of-the-art machine learning classification algorithms were evaluated for the Wisconsin Breast Cancer Dataset (WBCD). The three best classifiers were then selected based on their F3 score. F3 score is used to emphasize the importance of false negatives (recall) in breast cancer classification. Then, these three classifiers, simple logistic regression learning, support vector machine learning with stochastic gradient descent optimization and multilayer perceptron network, are used for ensemble classification using a voting mechanism. We also evaluated the performance of hard and soft voting mechanism. For hard voting, majority-based voting mechanism was used and for soft voting we used average of probabilities, product of probabilities, maximum of probabilities and minimum of probabilities-based voting methods. The hard voting (majority-based voting) mechanism shows better performance with 99.42%, as compared to the state-of-the-art algorithm for WBCD. |
| first_indexed | 2024-03-10T19:31:27Z |
| format | Article |
| id | doaj.art-47afcc994ac24a4d90465b60efcb563b |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-10T19:31:27Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-47afcc994ac24a4d90465b60efcb563b2023-11-20T02:06:31ZengMDPI AGJournal of Imaging2313-433X2020-05-01663910.3390/jimaging6060039Breast Tumor Classification Using an Ensemble Machine Learning MethodAdel S. Assiri0Saima Nazir1Sergio A. Velastin2College of Business, King Khalid University, Abha 62529, Saudi ArabiaDepartment of Software Engineering, Fatima Jinnah Women University, The Mall Rawalpindi, Punjab 46000, PakistanApplied Artificial Intelligence Research Group, Department of Computer Science, Universidad Carlos III de Madrid, 28270 Colmenarejo, SpainBreast cancer is the most common cause of death for women worldwide. Thus, the ability of artificial intelligence systems to detect possible breast cancer is very important. In this paper, an ensemble classification mechanism is proposed based on a majority voting mechanism. First, the performance of different state-of-the-art machine learning classification algorithms were evaluated for the Wisconsin Breast Cancer Dataset (WBCD). The three best classifiers were then selected based on their F3 score. F3 score is used to emphasize the importance of false negatives (recall) in breast cancer classification. Then, these three classifiers, simple logistic regression learning, support vector machine learning with stochastic gradient descent optimization and multilayer perceptron network, are used for ensemble classification using a voting mechanism. We also evaluated the performance of hard and soft voting mechanism. For hard voting, majority-based voting mechanism was used and for soft voting we used average of probabilities, product of probabilities, maximum of probabilities and minimum of probabilities-based voting methods. The hard voting (majority-based voting) mechanism shows better performance with 99.42%, as compared to the state-of-the-art algorithm for WBCD.https://www.mdpi.com/2313-433X/6/6/39breast cancer tumorclassificationmajority-based voting mechanismmultilayer perceptron learning networksimple logistic regressionstochastic gradient descent learning |
| spellingShingle | Adel S. Assiri Saima Nazir Sergio A. Velastin Breast Tumor Classification Using an Ensemble Machine Learning Method Journal of Imaging breast cancer tumor classification majority-based voting mechanism multilayer perceptron learning network simple logistic regression stochastic gradient descent learning |
| title | Breast Tumor Classification Using an Ensemble Machine Learning Method |
| title_full | Breast Tumor Classification Using an Ensemble Machine Learning Method |
| title_fullStr | Breast Tumor Classification Using an Ensemble Machine Learning Method |
| title_full_unstemmed | Breast Tumor Classification Using an Ensemble Machine Learning Method |
| title_short | Breast Tumor Classification Using an Ensemble Machine Learning Method |
| title_sort | breast tumor classification using an ensemble machine learning method |
| topic | breast cancer tumor classification majority-based voting mechanism multilayer perceptron learning network simple logistic regression stochastic gradient descent learning |
| url | https://www.mdpi.com/2313-433X/6/6/39 |
| work_keys_str_mv | AT adelsassiri breasttumorclassificationusinganensemblemachinelearningmethod AT saimanazir breasttumorclassificationusinganensemblemachinelearningmethod AT sergioavelastin breasttumorclassificationusinganensemblemachinelearningmethod |