PREDICTION OF POLYCYSTIC OVARIAN SYNDROME WITH CLINICAL DATASET USING A NOVEL HYBRID DATA MINING CLASSIFICATION TECHNIQUEJournal: International Journal of Advanced Research in Engineering and Technology (IJARET) (Vol.11, No. 11)
Publication Date: 2020-11-30
Authors : Neetha Thomas A. Kavitha;
Page : 1872-1881
Keywords : PCOS; Machine learning; Navies Bayes; Artificial neural network; Hybrid structure.;
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.
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