An immense need has emerged in several areas of biological area for the development of prediction algorithms capable of managing the increasing complexity of high-dimensional information. In developing countries, Cervical Cancer (CC) kills more women than any other disease or accident, and it's the top cause of death among women worldwide. Early detection and treatment lead to improved results and longer patient survival, which in turn reduces cancer mortality. For the majority of real-world data science problems, not all dataset variables are useful for building models. The accuracy of a classifier and the model's ability to generalize are both reduced by repeated variables. Furthermore, adding more variables increases the overall complexity of a model. Deep learning's feature selection approach is a good fit for this issue. When it comes to selecting features for linear regression, our novel Bilevel Optimized Recursive Feature Eliminator (BORFE) method represents a revolutionary development. Finding the optimal fit for a model and eliminating its most undesirable features is the objective of this innovative feature selection method. This study presents a novel cross-validation approach using a Bilevel Optimization–Based -Recursive Feature Extractor (BORFE) to perform an in-depth analysis of the hyper-parameters. When used with cross-validation, RFE finds the optimal number of features and the optimum selection of ranking features. According to the evaluation metrics, BORFE performs better than the other conventional algorithms when it comes to FS on cervical cancer datasets. For the cervical cancer dataset, this shows that BORFE can solve FS problems successfully
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