Fuzzy reasoning for medical diagnosis based on subjective attributes and objective attributes alignment

Issues on fuzzy reasoning by aggregating two types of ontologies that are used to formalize a patient state: mental ontology reflecting the patient mental behavior due to certain disorder and physical ontology reflecting the observed physical behavior exhibited through disorder. Similarity matching is used to find the similarity between fuzzy set reflected to mental fuzzy attributes (subjective type attributes), and physical fuzzy attributes (treated as objective attributes). The alignment is projected on medical ontology to rank attributes for decision making, by aggregating these attributes using intuitionistic fuzzy based operators. We apply aggregate functions for ranking objectives attributes related to physical symptoms. In the same time, we apply aggregate function for ranking subjective attributes related to mental patient subjective symptoms. The alignment of these two aggregate functions produce weighted ranking order fuzzy set for medical decision making for diagnosis. Euclidian distance function is used for objective attributes, and Hamming distance function is used for measuring the subjective attributes. The paper highlights these issues as new challenges extending intelligent reasoning of Virtual Doctor System called as VDS we built in Japan.