RESEARCH ARTICLE
Fuzzy Decision Making in Medical Diagnosis Using an Advanced Distance Measure on Intuitionistic Fuzzy Sets

Decision making in medical diagnosis is becoming a vast research field in medical science. In the last few decades, the role of distance measure in decision science is significant. Various distance measures on IFSs have been developed by many researchers and used in medical diagnosis in last few decades.

Medical diagnosis is one of the major decision-making situation in medical science. From the existing research and knowledge in medical science, the major, as well as minor symptoms of almost all disease, are recorded. In the initial stage of any disease, the symptoms of the patients are observed carefully and comparing the symptoms one can suggest the disease may be suffered by the patient. Therefore, an attempt has been made to practice a fuzzy decision-making approach using intuitionistic fuzzy sets. In addition, a novel distance measure on IFSs has been introduced.

Uncertainty is a factor which makes the decision-making process more difficult in medical diagnosis. Everyone is different from the other at physical level as well as mental level, symptoms of a disease expressed by patients are linguistic in nature and vary person to person. For example, the pain suffered in stomach by a patient usually expressed in linguistic expressions such as much, too much, severe etc. From the described symptoms by patients, a physician decides possible disease suffered by the patients. For this reason, in medical diagnosis, decision-making under uncertainty arises.

In this paper, a new distance measure between two Intuitionistic Fuzzy Sets (IFSs) has been proposed. The proposed distance measure has been used in a decision-making problem of a medical diagnosis problem. In addition, the result has been analyzed by comparing with the existing result of medical diagnosis.

The result obtained by the case study carried out with the help of the proposed distance measure is analyzed by comparing results with the existing result of medical diagnosis. This is found to be very much similar to the existing result of medical diagnosis. This concludes the efficiency and reliability of our proposed distance measure.