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Designs 2018, 2(2), 13; https://doi.org/10.3390/designs2020013

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

1
Department of Computer Science, Brunel University London, Uxbridge, Middlesex UB8 3PH, UK
2
School of Mathematics, Southeast University, Nanjing 210096, China
*
Author to whom correspondence should be addressed.
Received: 2 February 2018 / Revised: 22 April 2018 / Accepted: 23 April 2018 / Published: 9 May 2018
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Abstract

Breast cancer (BC) is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today’s society. The early diagnosis of BC can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients. Further accurate classification of benign tumours can prevent patients undergoing unnecessary treatments. Thus, the correct diagnosis of BC and classification of patients into malignant or benign groups is the subject of much research. Because of its unique advantages in critical features detection from complex BC datasets, machine learning (ML) is widely recognised as the methodology of choice in BC pattern classification and forecast modelling. In this paper, we aim to review ML techniques and their applications in BC diagnosis and prognosis. Firstly, we provide an overview of ML techniques including artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and k-nearest neighbors (k-NNs). Then, we investigate their applications in BC. Our primary data is drawn from the Wisconsin breast cancer database (WBCD) which is the benchmark database for comparing the results through different algorithms. Finally, a healthcare system model of our recent work is also shown. View Full-Text
Keywords: breast cancer; machine learning; artificial neural networks; decision tree; support vector machine; k-nearest neighbor; healthcare system; Wisconsin breast cancer database breast cancer; machine learning; artificial neural networks; decision tree; support vector machine; k-nearest neighbor; healthcare system; Wisconsin breast cancer database
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Yue, W.; Wang, Z.; Chen, H.; Payne, A.; Liu, X. Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis. Designs 2018, 2, 13.

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