Knowledge extraction from microarray datasets using combined multiple models to predict leukemia types.

Recent advances in microarray technology offer the ability to measure expression levels of thousands of genes simultaneously. Analysis of such data helps us identifying different clinical outcomes that are caused by expression of a few predictive genes. This chapter not only aims to select key predictive features for leukemia expression, but also demonstrates the rules that classify differentially expressed leukemia genes. The feature extraction and classification are carried out with combination of the high accuracy of ensemble based algorithms, and comprehensibility of a single decision tree. These allow deriving exact rules by describing gene expression differences among significantly expressed genes in leukemia. It is evident from our results that it is possible to achieve better accuracy in classifying leukemia without sacrificing the level of comprehensibility.