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Enhanced Fuzzy Rule Based Diagnostic Model for Lung Cancer using Priority Values

In this paper we design a fuzzy rule based medical model to detect and diagnose lung cancer. The disease is determined by using a rule base, populated by rules made for different types of lung cancer. The algorithm uses the output of the rule base (i.e. the disease name) and the symptoms entered by the user; it also uses the priority and severity values to determine the stage of cancer the patient is in. Both these results (disease name and stage) help the diagnostic logic to determine the treatment for the patient with accuracy. Our medical diagnosis deals with a complex analysis of all the information gathered about our symptoms. Domain expert’s knowledge is gathered to generate rules and stored in the rule base and the rules are fired when there exist appropriate symptoms. The system is implemented for the medical diagnosis and treatment for the patients as well as it can be used to assist the doctors. Keywords— Fuzzy rule base, medical diagnosis, priority value, stages and lung cancer.