Predicting Breast Cancer Survivability Using Random Forest and Multivariate Adaptive Regression Splines
In this paper, we propose a hybrid of random forest and multivariate adaptive regression splines algorithms for building a breast cancer survivability prediction model. We use random forest to perform a preliminary screening of variables and to receive a importance ranks. Then, the new dataset is extracted from initial WDBC dataset according to top-k important predictors and is input into the MARS procedure, which is responsible for building interpretable models for predicting breast cancer survivability. The capability of this combination method is evaluated using basic performance measurements (e.g., accuracy, sensitivity, and specificity) along with a 10-fold cross-validation. Experimental results show that the proposed method provides a higher accuracy and a relatively simple model.
Breast cancer random forest multivariate adaptive regression spline
Dengju Yao JingYang Xiaojuan Zhan
College of Computer Science and Technology, Harbin Engineering University, Harbin Heilongjiang, Chin College of Computer Science and Technology, Harbin Engineering University, Harbin Heilongjiang, Chin Department of Computer Science and Technology, Heilongjiang Institute of Technology, Harbin Heilongj
国际会议
哈尔滨
英文
2204-2207
2011-08-12（万方平台首次上网日期，不代表论文的发表时间）