Polycystic ovary syndrome detection using neural network.

Abstract

A fairly frequent endocrine abnormality among women of reproductive age is polycystic ovary syndrome (PCOS). In this disease, the ovaries produce abnormally high levels of androgens, which are male sex hormones that are typically present in women in trace amounts. The basic difference between PCOS and normal ovarian cysts is the substantial hormonal imbalance, which is not a general occurrence in ovarian cysts. A study says that among 15 percent of reproductive women, this disease is found, which is a major cause of women’s infertility. Even though this is a very common and widely spread serious disease worldwide, it is hard to diagnose properly. So firstly, since this is a worldwide problem, a lot of people are thinking, but they cannot come to a conclusion. Secondly, detecting this disorder is very difficult since the symptoms of PCOS match those of other diseases, which makes detection difficult. For this reason, we became interested in this area.