Comparison of Triangular and Trapezoidal Fuzzy Membership Function

Abstract

Classification is one of the important process in
clinical trial. Diagnosis leads to analyze the classification
of disease and their characters. Medical practitioner
makes decision from diagnosis result that is classified.
The result of diagnosis has classified based on the clinical
trials. The accuracy of classification leads to improve the
accuracy of diagnosis. Medical practitioner or research
takes decision about experiment, according to the clinical
trial reports. Most of the clinical trial reports and results
are in uncertainty and vagueness form. In data
preparation process, the vagueness value of clinical data
has to be change in to crisp form. The fuzzy logic assists
to formalize the vagueness value and prepare the data for
mining the knowledge. Fuzzy membership function
exercises the vagueness value that is in the range between
0 and 1. The objective of this paper is to fuzzify the
patient data using triangular and trapezoidal
membership function and mining the valuable
information. The output of triangular and trapezoidal
membership function shows the fuzzified clinical data
and measure the performance through mean value of
fuzzified data.