Abstract
The use of Fuzzy Expert System has highly increased in the field of medicine, to diagnosis the illness of patient pursuit. By applying the intensified fuzzy verdict mechanism the diagnosis of diabetes becomes simple for medical practitioners. The intensified fuzzy verdict mechanism consists of fuzzy inference, implication and aggregation. For the diagnosis of diabetes, knowledge are represented in the form of fuzzification to convert crisp values into fuzzy values. This mechanism, contains set of rules with fuzzy operators. Defuzzification method is adopted to convert the fuzzy values into crisp values. In this paper, intensified fuzzy verdict mechanism is proposed to complete the knowledge representation and the inference model for diabetes data. The result of the proposed methods is compared with earlier method using accuracy as metrics. This mechanism is focused on increasing the accuracy and quality of knowledge for diabetes application.
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Senthil Kumar, A.V., Kalpana, M. (2011). Communications in Computer and Information Science: Diagnosis of Diabetes Using Intensified Fuzzy Verdict Mechanism. In: Abd Manaf, A., Sahibuddin, S., Ahmad, R., Mohd Daud, S., El-Qawasmeh, E. (eds) Informatics Engineering and Information Science. ICIEIS 2011. Communications in Computer and Information Science, vol 253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25462-8_11
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DOI: https://doi.org/10.1007/978-3-642-25462-8_11
Publisher Name: Springer, Berlin, Heidelberg
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