Automatic Diagnosis of Polycystic Ovarian Syndrome Using Wrapper Methodology with Deep Learning Techniques

One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome (PCOS). Consequently, timely screening of polycystic ovarian syndrome can help in the process of recovery. Finding a method to aid doctors in this procedure was crucial due to the difficulties in detecting this condition. This research aimed to determine whether it is possible to optimize the detection of PCOS utilizing Deep Learning algorithms and methodologies. Additionally, feature selection methods that produce the most important subset of features can speed up calculation and enhance the effectiveness of classifiers. In this research, the tri-stage wrapper method is used because it reduces the computation time. The proposed study for the Automatic diagnosis of PCOS contains preprocessing, data normalization, feature selection, and classification. A dataset with 39 characteristics, including metabolism, neuroimaging, hormones, and biochemical information for 541 subjects, was employed in this scenario. To start, this research pre-processed the information. Next for feature selection, a tri-stage wrapper method such as Mutual Information, ReliefF, Chi-Square, and Xvariance is used. Then, various classification methods are tested and trained. Deep learning techniques including convolutional neural network (CNN), multi-layer perceptron (MLP), Recurrent neural network (RNN), and Bi long short-term memory (Bi-LSTM) are utilized for categorization. The experimental finding demonstrates that with effective feature extraction process using tri stage wrapper method + CNN delivers the highest precision (97%), high accuracy (98.67%), and recall (89%) when compared with other machine learning algorithms.