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 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.