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Prediction and Monitoring Method for Breast Cancer: A Case Study for Data from the University Hospital Centre of Coimbra

Authors Yue J, Zhao N, Liu L

Received 12 December 2019

Accepted for publication 29 February 2020

Published 13 March 2020 Volume 2020:12 Pages 1887—1893


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Antonella D'Anneo

Jin Yue, 1, 2,* Na Zhao, 3,* Liu Liu 1

1School of Mathematics and VC & VR Key Laboratory of Sichuan Province, Sichuan Normal University, Chengdu, People’s Republic of China; 2School of Mathematics, Sichuan University of Arts and Science, Dazhou, People’s Republic of China; 3Department of Clinical Laboratory and Guangdong Provincial Key Laboratory of Occupational Disease Prevention and Treatment, Guangdong Province Hospital for Occupational Disease Prevention and Treatment, Guangzhou, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Liu Liu

Abstract: Breast cancer is the second most common cancer in women after skin cancer. Breast cancer can occur in both men and women, but it is far more common in women. Real-time monitoring of breast cancer indicators is becoming increasingly important. It can help create advances in the diagnosis and treatment of breast cancer. In this paper, we provide a nonparametric statistical method to predict and detect breast cancer occur. The exponentially weighted moving average (EWMA) control scheme is based on rank methods so that it is completely nonparametric. It is efficient in detecting the shifts for multivariate processes. A real example data from the University Hospital Centre of Coimbra is given to illustrate this method.

Keywords: nonparametric, EWMA, rank-based method, breast cancer

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