Multi-Agent based Intelligent Decision Support Systems for Cancer Classification

There is evidence that early detection of cancer diseases can improve the treatment and increase the survival rate of patients. This paper presents an efficient CAD system for cancer diseases diagnosis by gene expression profiles of DNA microarray datasets. The proposed CAD system combines Intelligent Decision Support System (IDSS) and Multi-Agent (MA) system. The IDSS represents the backbone of the entire CAD system. It consists of two main phases; feature selection/reduction phase and a classification phase. In the feature selection/reduction phase, eight diverse methods are developed. While, in the classification phase, three evolutionary machine learning algorithms are employed. On the other hand, the MA system manages the entire operation of the CAD system. It first initializes several IDSSs (exactly 24 IDSSs) with the aid of mobile agents and then directs the generated IDSSs to run concurrently on the input dataset. Finally, a master agent selects the best classification, as the final report, based on the best classification accuracy returned from the 24 IDSSs The proposed CAD system is implemented in JAVA, and evaluated by using three microarray datasets including; Leukemia, Colon tumor, and Lung cancer. The system is able to classify different types of cancer diseases accurately in a very short time. This is because the MA system invokes 24 different IDSS to classify the diseases concurrently in parallel processing manner before taking the decision of the best classification result