Applications of medical information: Using an enhanced likelihood measured approach based on intuitionistic fuzzy sets

Abstract

Similarity measure is a key role in the analysis and research of medical diagnosis, pattern recognition, machine learning and clustering analysis in uncertainty environment. In this paper, we take a simple and intelligent approach, called intuitionistic fuzzy likelihood-based Measurement (IFLM), towards the medical diagnosis and bacteria classification problems. The proposed approach considers the information carried by the membership degree and the non-membership degree of intuitionistic fuzzy sets (IFSs) to examine its capability in encountering uncertainty in the medical pattern recognition. The observation from the results shows the usefulness of the proposed IFLM approach, it can provide the preliminary diagnosis for the doctors.