Assessment of the potential iridology for diagnosing kidney disease using wavelet analysis and neural networks

Alternative or complementary medicine emphasizes therapies that are claimed to improve quality of life, prevent disease, and address conditions that conventional medicine has limited success in curing. There are many techniques which are prevalent in many countries and these can cause harm if not scientifically evaluated. The objective of this paper is to validate the use of iridology to diagnose kidney abnormalities. Two subject groups were evaluated: one was 168 subjects free from kidney disease and the other was 172 subjects with chronic renal failure. The procedure to acquire, process and classify the iris images was designed in such a way that avoids any dependency on the iridologists by using wavelet analysis and Adaptive Neuro-Fuzzy Inference System. The results show a correct classification for both subjects with kidney problems and normal subjects of 82% and 93%, respectively. The proposed technique conducted on a systemic disease with ocular manifestations showed encouraging results. However, it is necessary to perform extensive studies with diseases that do not have ocular manifestations according to conventional medicine in order to validate iridology as a valid scientific technique.