Diagnosis of Diseases Based on Iridology Using Fuzzy Logic

(Zakaria Madhouse , Ammar Kayli and Luna Himmami)

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Abstract

Many automatic methods have been introduced in iridology to predict diseases according to the iridology chart. This is important to prevent diseases before they develop. This research aims to find a computer model for the early diagnosis of diseases in the brain, back, pelvis, abdomen, and chest using the iridology chart based on fuzzy logic. Image preprocessing for the iris aims to find the ring, code, and features of the iris. Five fuzzy models have been built for diagnosis and to determine a person's disease rate based on specific features that were extracted from the iris as the input variables. Each model contains four membership functions for each input or output variables and 64 fuzzy rules for fuzzification and defuzzification. The five models that were built to diagnose the five diseases of iridology have an accuracy rate of over 98%, with an average accuracy of 98.6223%. The results mean that the models are qualified for use by doctors as medical tools to diagnose specific diseases or as a tool for the public to reassure them about their health.

KEYWORDS
Abdominal, back, brain, chest, fuzzification, pelvic

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References

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