Op-RMSprop (Optimized-Root Mean Square Propagation) Classification for Prediction of Polycystic Ovary Syndrome (PCOS) using Hybrid Machine Learning Technique
Rakshitha Kiran P, N C Naveen
Abstract
Polycystic Ovary Syndrome is a common women's health problem caused by the imbalance in the reproductive hormones which causes problems in the ovaries. An appropriate machine learning (ML) algorithm can be applied to analyze the datasets and validate the performance of the algorithm in terms of accuracy. In this paper, a unique hybrid and optimized methodology are proposed which uses SVM linear kernel with Logistic Regression functionalities in a different way. The output of this model is passed on to the RMSprop optimizer. Optimization will train the model iteratively to get better output. For this research 1600 datasets were collected from the leading hospital in Bangalore Urban region. This optimized hybrid method is tested over PCOS datasets and it exhibited 89.03% accuracy. The results showed that the optimized-hybrid model works efficiently when compared to other existing ML Algorithms like SVM, Logistic regression, Decision tree, KNN, Random forest, and Adaboost. Also, the results of the optimized-hybrid SVLR model showed good results in terms of F-measure, precision, and recall statistical criteria. Overall this paper summarizes the working of the proposed optimized-SVLR hybrid model and prediction of PCOS.