Intelligence Ensemble Feature Selection (IEFS) and Ensemble Classifier for Cervical Cancer Diagnosis

Cervical cancer is one of the most common diseases affecting women, which may be prevented with an early diagnosis. Numerous recent studies using DL and ML techniques have focused on early-stage prediction of this disease. DL methods, it's critical to understand the importance of factors for appropriately classifying patients with cervical cancer. In other words, FS selects the most important features from the dataset when building a DL model and introduces IEFS and DLE for cervical cancer. IEFS has been introduced to choose the important features and remove redundant features to train a model using the DLE classifier. The IEFS model combines the results of methods like EBFO, EEHO, and KLBWO, creating an optimal best subset. An aggregation function has combined the results of individual methods. DLE is a DL approach that combines several methods (GAN, BGRU, and DWCNN) in cancer diagnosis. The LogitBoost model combines the output from different classifiers. UCI is used to collect cervical cancer (risk factors) to measure the results of the proposed classifier against current classifiers. MATLABR2020a has been used to simulate and measure the classifiers using precision, recall/sensitivity, F-measure, specificity, and accuracy metrics.