Description

A fuzzy expert system is a collection of membership functions and rules that are used to reason about data. Despite great advances in discovering cancer molecular profiles, the proper application of microarray technology to routine clinical diagnostics is still a challenge. Current practices in the classification of microarrays’ data show two main limitations: the reliability of the training data sets used to build the classifiers, and the classifiers’ performances, especially when the sample to be classified does not belong to any of the available classes. Medical thermography has proved to be useful in various medical applications including the detection of breast cancer where it is able to identify the local temperature increase caused by the high metabolic activity of cancer cells. It has been shown to be particularly well suited for picking up tumors in their early stages or tumors in dense tissue and outperforms other modalities such as mammography for these cases. Unlike conventional expert systems, which are mainly symbolic reasoning engines, fuzzy expert systems are oriented toward numerical processing. To address the interpretability-accuracy tradeoff, the system proposes hybrid Ant Bee Algorithm (ABA) and it is evaluated using six gene expression data sets.