Prediction of Pcod Among Working Women in it Sector Using Healthcare Data Set

Infertility and abortions are becoming common. among women day by day. PCOS (Poly Cystic Ovarian Syndrome) or PCOD (Poly Cystic Ovary Disorder) is one of the major endocrine disorders among women of reproductive age. It is characterized by hormone imbalance. The affected women have a higher concentration of male hormone than female hormone. The warning signs of this disease include irregular menses, oily skin, acne, hypertension and a high risk of mood and anxiety disorders. A recent survey has revealed that PCOD is more likely to occur among working women, especially employed in IT sector. In general, there is no prescribed medicine or treatment for this lifestyle disease. Early detection and its prevention are inevitable. So. adoption of a healthy lifestyle could mitigate the ill effects. The time and the amount spent on conducting clinical tests and scanning the ovaries create a lot of burden for these women. To bring an efficient solution to this issue, this paper suggests a prediction model with an optimum accuracy for early identification and prediction of PCOS from a set of promising clinical, physical and metabolic parameters, which act as early markers for this syndrome. Data-mining tools and techniques, along with machine learning [1], can be used among different sections for an effective data prediction