Design and implementation of fuzzy rule-based expert system for endocrine glands disease diagnosis using particle swarm optimization algorithm

Abstract EN

Endocrinology and metabolism is the branch of medicine concerned with the study of the diseases of the endocrine organs, disorders of hormone systems, and their target organs and disorders of the pathways of glucose and lipid metabolism in health and disease.

Medical diagnosis is an important but complicated task that should be performed accurately and efficiently and its automation would be very usefulin this domain (medical domain).

It encompasses the assessment of patients with such disorders and the use of laboratory methods for diagnosis and monitoring of therapy.

In this project we will design expert system model to detect and diagnose endocrinology disease.

The disease is determined by using a Particle Swarm Optimization Algorithm.

The objective of this algorithm is to obtain a solution and result optimization and better by matching the rules resulting from the fuzzy logic with one of the rules of the disease, means that every rule of the disease represents the best solution - via simulate the behavior of birds in search of the best food ,Thus, any system based on this algorithm will be shaped in the beginning from the random aggregation (the rule resulting from fuzzy) of random solutions, and search within this assembly a perfect solution(looking for rule most match with one of the diseases) and by updating generations .