PREDICTION OF POLYCYSTIC OVARIAN SYNDROME WITH CLINICAL DATASET USING A NOVEL HYBRID DATA MINING CLASSIFICATION TECHNIQUE

Polycystic Ovary Syndrome (PCOS) is a female hormone disorder which sorely affects women health by developing symptoms like irregular menses, infertility, obesity, hyperandrogenism, alopecia, may lead to another severe health risk like metabolic syndrome, type 2 diabetes mellitus, Cardio Vascular Diseases etc. It affects 5-10% of women in their puberty. The objective of the study is to predict the occurrence of PCOS before getting worse. Data mining holds excellent prospective to ameliorate health care; several studies have already undergone to foreshadow the danger of PCOS in women. The proposed system is a novel hybrid structure to determine the chances of PCOS that coalesce navies Bayes and artificial neural network algorithm to produce the best result.