Improving the prediction accuracy of liver disorder disease with oversampling
AMERICAN-MATH'12/CEA'12 Proceedings of the 6th WSEAS international conference on Computer Engineering and Applications, and Proceedings of the 2012 American conference on Applied Mathematics
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In spite of all the standardization methods in medical diagnosis, a correct diagnosis is still considered to be an art .much of this situation is for, that medical diagnosis needs proficiency as well as experience in dealing with uncertainty. Although, in our mechanized age, boundaries of medical science have extremely expanded, you can not overcome this uncertainty easily. Offering a powerful framework to construct the model of existing systems causes fuzzy theory to change to a valuable factor towards medical diagnosis improvement. In this research, a fuzzy system has been designed for learning, analysis and diagnosis of liver disorders. Required data has been chosen from trusty database (UCI) that has 345 records and 6 fields as the entrance parameters and rate of liver disorder risks is used as the system resulting. This system in comparison with other traditional diagnostic systems is faster, cheaper, and also more liable and more accurate. One can uses this system as a specialist assistant or for training medicine students. Also,on time diagnosis of disease and appointing the rate of liver disorders improvement has been experienced and its Verification 91%.