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 surgi...

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Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.78242-78252
Main Authors: Naseem, Usman, Rashid, Junaid, Ali, Liaqat, Kim, Jungeun, Haq, Qazi Emad Ul, Awan, Mazhar Javed, Imran, Muhammad
Format: Article
Language:English
Subjects:
Algorithms
Breast
Breast cancer
cancer diagnoses
Classification algorithms
Classifiers
Datasets
Diagnosis
ensemble learning
Healthcare system
Machine learning
Mammography
Medical research
Prognosis
Prognostics and health management
Support vector machines
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3174599