Different health care societies & hospitals have different kind of system which has been used for data storage of each recapitulated patient's disease. However patient's disease is increasing, day by day. So there is a need of an application which can provide information for decision makers, on patient diseases collected data. Computational intelligence methods open up new prospects for diseases diagnostic criteria. Data mining is an approach which can help in decision making. Hybriddimension association rules based, data mining technique, is based on methodologies for analyzing the relationship between diseases with patient characteristics. Apriori algorithm based data mining can support for development of this type of methodologies. Hybrid dimension association rule is a multidimensional association rule that allows the repetition of the predicate. Each rule can be used to describe the rules of the relationship with the patient's disease and patient's characteristics. In the proposed approach an extendable and improved item set generation has been constructed, and developed, for mining the relationships of the symptoms and disorder in the medical databases. It will produce hybrid dimension association rules and the rules have been displayed in form of tables and graphs
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.