Implementing Neural Fuzzy Rough Set and Artificial Neural Network for Predicting PCOS

Abstract

Polycystic ovarian syndrome (or polycystic ovary syndrome – PCOS) is a multifarious form in which a woman’s ovaries are normally larger than standard. The term ‘Polycystic’ defines that the ovaries comprise of numerous cysts or follicles to facilitate hardly ever nurture towards ripeness or generate eggs accomplished of being fertilized. One third of women could contain polycystic ovaries observed on an ultrasound, however it does not all have PCOS. PCOS is comparatively universal, especially for sterile women. It concerns about 12 to 18 per cent of women of reproductive age (between late adolescence and menopause). In approximate 70 per cent of this kind of cases remain undiagnosed. In our previous researches, we have proposed a new feature selection technique and hybrid approach and in this present investigation, we implement these proposed algorithms to forecast the PCOS disease among women. In addition to above analysis, we evaluate the effect of the proposed algorithms with other existing methods.