Adaptive Fuzzy Association Rule mining for effective decision support in biomedical applications.

Due to complexity of biomedical classification problems, it is impossible to build a perfect classifier with 100% prediction accuracy. Hence a more realistic target is to build an effective 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 novel adaptive Fuzzy Association Rules (FARs) mining algorithm, named FARM-DS, to build such a DSS for binary classification problems in the biomedical domain. In the training phase, four steps are executed to mine FARs, which are thereafter used to predict unseen samples in the testing phase. The new FARM-DS algorithm is evaluated on two publicly available medical datasets. The experimental results show that FARM-DS is competitive in terms of prediction accuracy. More importantly, the mined FARs provide strong decision support on disease diagnoses due to their easy interpretability.