An Automatic Detection of Breast Cancer Diagnosis and Prognosis based on Machine Learning Using Ensemble of Classifiers

• Main Authors: Ali, L. (Author), Awan, M.J (Author), Haq, Q.E.U (Author), Imran, M. (Author), Kim, J. (Author), Naseem, U. (Author), Rashid, J. (Author)
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2022
• Subjects: Breast; breast cancer; Breast cancer; Breast Cancer; Breast cancer diagnosis; Cancer diagnoses; Cancer diagnosis; Classification (of information); Classification algorithm; Classification algorithms; Diagnosis; Diseases; Ensemble learning; Health care; Healthcare system; Healthcare systems; Learning algorithms; machine learning; Machine learning; Mammography; Patient rehabilitation; Prognostic and health management; Prognostics and health management; Support vector machines; Support vectors machine
• Online Access: View Fulltext in Publisher

 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. Author