Due to the difficulties of outlier and skewed data, the prediction of breast cancer survivability has presented many challenges in the field of data mining and pattern precognition, especially in medical research. To solve these problems, we have proposed a hybrid approach to generating higher quality data sets in the creation of improved breast cancer survival prediction models. This approach ...

Breast cancer is one of the deadliest diseases, is the most common of all cancers and is the leading cause of cancer deaths in women worldwide. The classification of Breast Cancer data can be useful to predict the outcome of some diseases or discover the genetic behavior of tumors. In this paper we present a comparative survey on data mining techniques in the diagnosis and prediction of breast ...

An accurate breast cancer prognosis, or breast cancer survivability prediction, is important as it often guides the treatment course of action, ability to claim additional financial support from the government, actions of the patient and family, and more [1]. Predicting breast cancer survivability is commonly done using clinical features. TNM staging, the globally accepted standard used to desc...

Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of machine learning. In this paper we have used an approach by using support vector machine classifier to construct a model that is useful for the breast cancer survivability prediction. We have used both 5 cross and 10 cross validation of variable selection on input feature vectors and the perfo...

OBJECTIVE The prediction of breast cancer survivability has been a challenging research problem for many researchers. Since the early dates of the related research, much advancement has been recorded in several related fields. For instance, thanks to innovative biomedical technologies, better explanatory prognostic factors are being measured and recorded; thanks to low cost computer hardware an...

This paper presents a classifier ensemble approach for predicting the survivability of the breast cancer patients using the latest database version of the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute. The system consists of two main components; features selection and classifier ensemble components. The features selection component divides the featu...

normal 0 false false false en-us x-none ar-sa microsoftinternetexplorer4 background: ca 15.3 is elevated in most patients with distant metastatic breast cancer who had prognostic information. the present study was performed to estimate predictive ability of ca 15.3 in assessment of symptomatic metastasis in patients with breast cancer. materials and methods: during five years, 159 primary breas...

many studies suggested that fractals could be brought in any structural anarchy, such as cancers, for diagnosis and prognosis evaluation by quantifying it and expressing it in terms of fractal geometry. in this study, we aimed to evaluate pathologic grades or breast carcinomas by fractal analysis. this cross-sectional study was performed on 60 women with breast ductal carcinoma. from all the pa...