Comparison of Triangular and Trapezoidal Fuzzy Membership Function - S, Princy.; Dhenakaran, Dr.S.S. (IJRDO -Journal of Computer Science Engineering, 2016)
Classification is one of the important process inclinical trial. Diagnosis leads to analyze the classificationof disease and their characters. Medical practitionermakes decision from diagnosis result that is classified.The result of diagnosis has classified based on the clinicaltrials. The accuracy of classification leads to improve theaccuracy of diagnosis. Medical practitioner or researchtakes decision about experiment, according to the clinicaltrial reports. Most of the clinical trial reports and resultsare in uncertainty and vagueness form. In datapreparation process, the vagueness value of clinical datahas to be change in to crisp form. The fuzzy logic assiststo formalize the vagueness value and prepare the data formining the knowledge. Fuzzy membership functionexercises the vagueness value that is in the range between0 and 1. The objective of this paper is to fuzzify thepatient data using triangular and trapezoidalmembership function and mining the valuableinformation. The output of triangular and trapezoidalmembership function shows the fuzzified clinical dataand measure the performance through mean value offuzzified data.