Predicting breast cancer survivability: a comparison of three data mining methods

作者:

Highlights:

摘要

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 and software technologies, high volume better quality data is being collected and stored automatically; and finally thanks to better analytical methods, those voluminous data is being processed effectively and efficiently. Therefore, the main objective of this manuscript is to report on a research project where we took advantage of those available technological advancements to develop prediction models for breast cancer survivability.

论文关键词:Breast cancer survivability,Data mining,k-Fold cross-validation,SEER

论文评审过程:Received 13 January 2004, Revised 30 June 2004, Accepted 15 July 2004, Available online 9 September 2004.

论文官网地址:https://doi.org/10.1016/j.artmed.2004.07.002