IMPROVING THE EFFICIENCY OF MEDICAL EXPERT SYSTEM
USING CLUSTERING AND FUZZY LOGIC APPROACHES
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In this research we are introducing a novel approach to clustering data. Two or more records identified separately actually represent the same real world entity. Data clustering is the process in which groups of the records are not predefined. The proposed technique takes input symptoms form the doctors and extract the disease and its related multiple profiles of disease treatment stored in data ware house. But using clustering approach it divides all profiles into the related groups. Expert system only select three best ranking profiles by using one-way clustering and finally the expert syste...
In this research we are introducing a novel approach to clustering data. Two or more records identified separately actually represent the same real world entity. Data clustering is the process in which groups of the records are not predefined. The proposed technique takes input symptoms form the doctors and extract the disease and its related multiple profiles of disease treatment stored in data ware house. But using clustering approach it divides all profiles into the related groups. Expert system only select three best ranking profiles by using one-way clustering and finally the expert system extract the treatment of the disease using two-way clustering. Data clustering is very crucial for business as information is the life blood of every organization. If data having dirt that miss guides the analysts during decision making which is disaster for the health of businesses.
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