scholarly journals INTCARE - Multi-agent Approach for Real-time Intelligent Decision Support in Intensive Medicine

2021 ◽  
Author(s):  
Hanaa Salem ◽  
Gamal Attiya ◽  
Nawal El-Fishawy

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.


2012 ◽  
Vol 271-272 ◽  
pp. 1608-1613
Author(s):  
Yong Yong Sun

The majority of work is completed by collaboration of various components in a particular group environment, and the architecture of Multi-Agent Intelligent Decision Support System based on blackboard is designed using blackboard system and Agent. At the same time, implementation technique on Agent in system is provided, that includes abstract structure of Agent and modeling of Agent action. In addition to, mechanism of breakdown the task on blackboard control Agent is described. The system is helpful to resolve the complicated works though Multi-Agent.


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