Detecting PCOS with a Novel Hybrid Classification Approach Using SMOTE and BiGRU-Maxout Models

PCOS is a type of endocrine dysfunction that impacts women's reproductive systems. This illness reduces the likelihood of becoming pregnant. The primary cause of this disease is the female body's suspiciously increased levels of the male hormone known as androgen. The normally occurring hormones in females are impacted by the aberrant function of this androgen. Atypical menstrual cycles, excessive menstrual blood, acne, and excessive weight are among the signs of PCOS. In certain cases, these conditions can worsen and result in major health issues like type 2 diabetes and ovarian cancer. Early PCOS detection may help manage the condition. In light of this, this work presents a hybrid model with an enhanced resampling technique for PCOS detection that consists of five steps. These include feature extraction and detection, data augmentation, data sampling, and preprocessing. Data normalisation is done as part of the preparation stage of data, which improves dataset quality for more insightful analysis. Afterwards, a better SMOTE framework is suggested for dataset sampling. The min-max data augmentation procedure is used to enhance the sampled data. Different features are then extracted, including those based on mutual information, statistics, and entropy. Lastly, a hybrid classification model that combines the deep maxout classification model and BiGRU is employed for the classification process. Lastly, several measures are used to assess the performance of the suggested task.