A Multi Layered Model for Polystic Syndrome Perception using CNMP

Abstract

In modern world, women’s facing several issues in society as well as some disorders in the human body. One of the most critical disorders is Polycystic ovary syndrome during their reproductive phase. This syndrome develop certain health problems includes with harmonical imbalance during reproductive stages. The too much of androgen male hormone deposit with many small sacs of fluid in the ovaries, this may fail to release the egg regularly. Literally said there is no chance of finding the cause of syndrome,since we want to detect the syndrome earlier stage. For that, machine learning techniques are useful to detect the Syndrome efficiently. The proposed methodologies CNMP (COMBINED NEURAL MULTI-LAYERED PERCEPTRON), have high accuracy to detect the early stages of Polycystic ovary syndrome and develop a user interface for easily test the syndrome. With this facing of problems in real world situation, girl can guess her severity using this approach.