Designing Intelligence E-health via training Artificial Neural Network and Fuzzy rules by diagnosis symptoms Viral and Infection disease in Iris of eyes

Iris-diagnosis or Iridology is a non-invasive way of obtaining information about the state of the internal structures, tissues and organs of the body from studying the iris (colored-part) of the eye. Iridology can help you understand what ‘optimum health’ means for an individual person, which displays a unique insight into our health potential and disease dispositions. Iris patterns and pigmentation reveal the history of our inherited health via e-health monitoring services .in this article, we represent innovative method for recognition symptoms of two dangerous diseases such as MALARIA and HIV. Then train the pattern extract with multi-layer feed forward neural network. Finally, we represented optimal model that can process parallel nodes which illustrate state of symptoms of disease and sorting string bits via genetic and EV algorithms. With simulation symptoms in iris zone and sectors, we design new intelligence monitoring e-health services that using fast and agile structure can recognize state of illness and will predict extra symptoms of next stage of illness.

Keywords:

Iris recognition , radius base neural network , genetic , monitoring e-health , Hopfield , Parallel , HIV , Malaria