Innovative Web Framework for Cervical Cancer Detection : A Machine Learning Advancement

Abstract

This research introduces a cutting-edge web framework specifically tailored for detecting cervical cancer using advanced machine learning techniques. The framework leverages a comprehensive dataset that encompasses demographic details, medical history, sexual behavior, contraceptive use, and previous medical diagnoses. By integrating multiple models, including AdaBoost, XGBoost, a stacking classifier, and logistic regression, the framework enhances the accuracy and reliability of cervical cancer diagnosis. The primary objective is to enable early detection and prompt intervention, which are crucial for improving patient outcomes in cervical cancer care. Through a thorough evaluation and comparison of these algorithms, the study demonstrates their effectiveness in predictive modeling for cervical cancer, marking a significant step forward in the application of machine learning in healthcare.