Multi-agent system for early prediction of urinary bladder inflammation disease.
ABSTRACT This paper presents an efficient real-time knowledge base architecture for multi-agent based patient diagnostic system for chronic disease management, basically, the early detection of Inflammation of urinary bladder and Nephritis of renal pelvis origin diseases. The model integrates information stored heterogeneous and geographically distributed healthcare centers. The paper presents two main contributions. First, a proposed multi-agent based system for mining frequent itemsets in distributed databases. Second, the implementation of this model on distributed medical databases in order to generate hidden medical rules. The proposed model can gather information from each department or from different hospitals, and using the cooperative agents it analyzes the data using association rules as a data mining technique. The proposed model improves the diagnostic knowledge and discovers the diseases based on the minimum number of effective tests, thus, providing accurate medical decisions based on cost effective treatments. It can also predict the existence or the absence of the diseases, thus improving the medical service for the patients. The proposed multi-agent system constitute an effort toward the design of intelligent, flexible, and integrated large-scale distributed data mining system.
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ABSTRACT: Many algorithms have been proposed for the discovery of association rules. The efficiency of these algorithms needs to be improved to handle real-world large datasets. Specifically, for data stored in heterogeneous and geographically distributed healthcare centers. This efficiency can be determined mainly by three factors. The way candidates are generated, the way their supports are counted and the data structure used. Most papers focus on the first and the second factors while few focus on the underlying data structures. In this paper, we present a distributed Multi-Agent based algorithm for mining association rules in distributed environments. The distributed MAS algorithm uses Bit vector data structure that was proved to have better performance in centralized environments. The algorithm is implemented in the context of Multi-Agent systems and complies with global communication standard Foundation for Intelligent Physical Agents (FIPA). The distributed Multi-Agent based algorithm with its new data structure improves implementations reported in the literature that were based on Apriori. The algorithm has better performance over Apriori-like algorithms.