Predicting the continuous values of breast cancer relapse time by type-2 fuzzy logic system

Microarray analysis and gene expression profile have been widely used in tumor classification, survival analysis and ER statues of breast cancer. Sample discrimination as well as identification of significant genes have been the focus of most previous studies. The aim of this research is to propose a fuzzy model to predict the relapse time of breast cancer by using breast cancer dataset published by van't Veer. Fuzzy rule mining based on support vector machine has been used in a hybrid method with rule pruning and shown its ability to divide the samples in many subgroups. To handle the existence of uncertainties in linguistic variables and fuzzy sets, the TSK model of Interval type-2 fuzzy logic system has been used and a new simple method is also developed to consider the uncertainties of the rules which have been optimized by genetic algorithm. B632 validation method is applied to estimate the error of the model. The results with 95 % confidence interval show a reasonable accuracy in prediction.