Mining fuzzy association rules from microarray gene expression data for leukemia classification
Yuanchen He, Yuchun Tang, Yanqing Zhang, Rajshekhar Sunderraman · 2006
Due to complexity of biomedical problems, it is difficult or even impossible to build a perfect model with 100% prediction accuracy. Hence a more realistic target is to build an Decision Support System (DSS). Here effective means that a DSS should not only predict unseen samples accurately, but also work in a human-understandable way. In this paper, we propose a new Fuzzy Association Rules (FARs) mining algorithm, named FARM-DS, to build such a DSS for binary classification problems in the biomedical field. In the training phase, four steps are used to mine FARs. These FARs are thereafter used to predict unseen samples in the testing phase. The new FARM-DS algorithm is compared with CART and ANFIS on AML/ALL leukemia dataset. The experimental results show that FARM-DS has high prediction accuracy. More importantly, due to their easy interpretability, the meaningful mined IF-THEN FARs can assist biomedical experts for further leukemia study.