An Automatic Detection of Breast Cancer Diagnosis and Prognosis Based on Machine Learning Using Ensemble of Classifiers
Breast cancer (BC) is the second most prevalent type of cancer among women leading to death, and its rate of mortality is very high. Its effects will be reduced if diagnosed early. BC’s early detection will greatly boost the prognosis and likelihood of recovery, as it may encourage prompt...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9773160/ |
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| author | Usman Naseem Junaid Rashid Liaqat Ali Jungeun Kim Qazi Emad Ul Haq Mazhar Javed Awan Muhammad Imran |
| author_facet | Usman Naseem Junaid Rashid Liaqat Ali Jungeun Kim Qazi Emad Ul Haq Mazhar Javed Awan Muhammad Imran |
| author_sort | Usman Naseem |
| collection | DOAJ |
| description | Breast cancer (BC) is the second most prevalent type of cancer among women leading to death, and its rate of mortality is very high. Its effects will be reduced if diagnosed early. BC’s early detection will greatly boost the prognosis and likelihood of recovery, as it may encourage prompt surgical care for patients. It is therefore vital to have a system enabling the healthcare industry to detect breast cancer quickly and accurately. Machine learning (ML) is widely used in breast cancer (BC) pattern classification due to its advantages in modelling a critical feature detection from complex BC datasets. In this paper, we propose a system for automatic detection of BC diagnosis and prognosis using ensemble of classifiers. First, we review various machine learning (ML) algorithms and ensemble of different ML algorithms. We present an overview of ML algorithms including ANN, and ensemble of different classifiers for automatic BC diagnosis and prognosis detection. We also present and compare various ensemble models and other variants of tested ML based models with and without up-sampling technique on two benchmark datasets. We also studied the effects of using balanced class weight on prognosis dataset and compared its performance with others. The results showed that the ensemble method outperformed other state-of-the-art methods and achieved 98.83% accuracy. Because of high performance, the proposed system is of great importance to the medical industry and relevant research community. The comparison shows that the proposed method outperformed other state-of-the-art methods. |
| first_indexed | 2024-04-12T08:25:38Z |
| format | Article |
| id | doaj.art-a967cd56ed244c619e825101c984a302 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T08:25:38Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a967cd56ed244c619e825101c984a3022022-12-22T03:40:23ZengIEEEIEEE Access2169-35362022-01-0110782427825210.1109/ACCESS.2022.31745999773160An Automatic Detection of Breast Cancer Diagnosis and Prognosis Based on Machine Learning Using Ensemble of ClassifiersUsman Naseem0https://orcid.org/0000-0003-0191-7171Junaid Rashid1https://orcid.org/0000-0002-1485-0757Liaqat Ali2Jungeun Kim3https://orcid.org/0000-0002-6474-5058Qazi Emad Ul Haq4https://orcid.org/0000-0003-1448-3632Mazhar Javed Awan5https://orcid.org/0000-0003-3535-8005Muhammad Imran6https://orcid.org/0000-0002-6946-2591Department of Information Technology, Sydney International School of Technology and Commerce, Sydney, NSW, AustraliaDepartment of Computer Science and Engineering, Kongju National University, Cheonan, South KoreaDepartment of Electrical Engineering, University of Science and Technology Bannu, Bannu, PakistanDepartment of Computer Science and Engineering, Kongju National University, Cheonan, South KoreaCenter of Excellence in Cybercrime and Digital Forensics (CoECDF), Naif Arab University for Security Sciences (NAUSS), Riyadh, Saudi ArabiaDepartment of Software Engineering, University of Management and Technology, Lahore, PakistanSchool of Engineering, Information Technology and Physical Sciences, Federation University, Brisbane, QLD, AustraliaBreast cancer (BC) is the second most prevalent type of cancer among women leading to death, and its rate of mortality is very high. Its effects will be reduced if diagnosed early. BC’s early detection will greatly boost the prognosis and likelihood of recovery, as it may encourage prompt surgical care for patients. It is therefore vital to have a system enabling the healthcare industry to detect breast cancer quickly and accurately. Machine learning (ML) is widely used in breast cancer (BC) pattern classification due to its advantages in modelling a critical feature detection from complex BC datasets. In this paper, we propose a system for automatic detection of BC diagnosis and prognosis using ensemble of classifiers. First, we review various machine learning (ML) algorithms and ensemble of different ML algorithms. We present an overview of ML algorithms including ANN, and ensemble of different classifiers for automatic BC diagnosis and prognosis detection. We also present and compare various ensemble models and other variants of tested ML based models with and without up-sampling technique on two benchmark datasets. We also studied the effects of using balanced class weight on prognosis dataset and compared its performance with others. The results showed that the ensemble method outperformed other state-of-the-art methods and achieved 98.83% accuracy. Because of high performance, the proposed system is of great importance to the medical industry and relevant research community. The comparison shows that the proposed method outperformed other state-of-the-art methods.https://ieeexplore.ieee.org/document/9773160/Healthcare systemmachine learningbreast cancerensemble learningcancer diagnoses |
| spellingShingle | Usman Naseem Junaid Rashid Liaqat Ali Jungeun Kim Qazi Emad Ul Haq Mazhar Javed Awan Muhammad Imran An Automatic Detection of Breast Cancer Diagnosis and Prognosis Based on Machine Learning Using Ensemble of Classifiers IEEE Access Healthcare system machine learning breast cancer ensemble learning cancer diagnoses |
| title | An Automatic Detection of Breast Cancer Diagnosis and Prognosis Based on Machine Learning Using Ensemble of Classifiers |
| title_full | An Automatic Detection of Breast Cancer Diagnosis and Prognosis Based on Machine Learning Using Ensemble of Classifiers |
| title_fullStr | An Automatic Detection of Breast Cancer Diagnosis and Prognosis Based on Machine Learning Using Ensemble of Classifiers |
| title_full_unstemmed | An Automatic Detection of Breast Cancer Diagnosis and Prognosis Based on Machine Learning Using Ensemble of Classifiers |
| title_short | An Automatic Detection of Breast Cancer Diagnosis and Prognosis Based on Machine Learning Using Ensemble of Classifiers |
| title_sort | automatic detection of breast cancer diagnosis and prognosis based on machine learning using ensemble of classifiers |
| topic | Healthcare system machine learning breast cancer ensemble learning cancer diagnoses |
| url | https://ieeexplore.ieee.org/document/9773160/ |
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