Toward breast cancer survivability prediction models through improving training space

  • Authors:

  • Jaree Thongkam;Guandong Xu;Yanchun Zhang;Fuchun Huang

  • Affiliations:

  • School of Computer Science and Mathematics, Victoria University, P.O. Box 14428, Melbourne, Vic. 8001, Australia;School of Computer Science and Mathematics, Victoria University, P.O. Box 14428, Melbourne, Vic. 8001, Australia;School of Computer Science and Mathematics, Victoria University, P.O. Box 14428, Melbourne, Vic. 8001, Australia;School of Computer Science and Mathematics, Victoria University, P.O. Box 14428, Melbourne, Vic. 8001, Australia

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2009

Quantified Score

Hi-index 12.05

Visualization

Abstract

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 comprises two main steps: (1) utilization of an outlier filtering approach based on C-Support Vector Classification (C-SVC) to identify and eliminate outlier instances; and (2) application of an over-sampling approach using over-sampling with replacement to increase the number of instances in the minority class. In order to assess the capability and effectiveness of the proposed approach, several measurement methods including basic performance (e.g., accuracy, sensitivity, and specificity), Area Under the receiver operating characteristic Curve (AUC) and F-measure were utilized. Moreover, a 10-fold cross-validation method was used to reduce the bias and variance of the results of breast cancer survivability prediction models. Results have indicated that the proposed approach leads to improving the performance of breast cancer survivability prediction models by up to 28.34% due to the improved training data space.