Open Access<p>Prediction and Monitoring Method for Breast Cancer: A Case Study for Data from the University Hospital Centre of Coimbra</p>
Author(s) -
Yaming Jin,
Na Zhao,
Liu Liu
Publication year - 2020
Publication title -
cancer management and research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.024
H-Index - 40
ISSN - 1179-1322
DOI - 10.2147/cmar.s242027
Subject(s) - breast cancer , ewma chart , medicine , china , cancer , nonparametric statistics , oncology , family medicine , statistics , mathematics , geography , computer science , control chart , process (computing) , operating system , archaeology
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.