• Renju K
• Pavithra B

Polycystic Ovary Syndrome (PCOS) is a condition diagnosed commonly in young women in their reproductive age. PCOS will cause prolonged or irregular menstrual periods because of the variations in androgen levels. Early detection of PCOS will reduce the risk of weight gain, mood swings, heart disease and other long-term complications. Researchers have proved that diagnosing PCOS at the early stage and undergoing treatment will reduce the risk factors in order to lead a normal life. The proposed research work focuses on diagnosing PCOS using deep learning techniques with optimal and minimal set of parameters. The paper also discusses about the preprocessing techniques used for dimensionality reduction and perform a comparative analysis of linear and non-linear methods. Artificial Neural Network model is used to predict the result based on different parameters and the result is evaluated with different layers in the network.

Polycystic ovary syndrome, Radial Basis Function, Multilayer Perceptron, Deep Learning models, Principal Component analysis, ISOMAP

"Comparative Analysis of Classifiers for Predicting Polycystic Ovary Syndrome Using Deep Learning Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.d291-d297, June-2022, Available :http://www.jetir.org/papers/JETIR2206337.pdf

"Comparative Analysis of Classifiers for Predicting Polycystic Ovary Syndrome Using Deep Learning Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. ppd291-d297, June-2022, Available at : http://www.jetir.org/papers/JETIR2206337.pdf