Artificial Intelligence in Disease Diagnostics: Rethinking Risk Factors for Cervical Cancer

As machine learning evolves, many individuals and businesses utilize numerous algorithms to evaluate massive datasets and create actionable insights that aid in predicting behavior. And this type of technology is increasingly employed in the medical industry to forecast the early stages of certain severe diseases, such as cervical cancer. There has been a significant amount of research conducted on cervical cancer in recent years. Studies have focused on various topics such as risk factors for cervical cancer, early detection and screening, and the effectiveness of different treatment options. In this study, we conduct an in-depth comparison of the various machine learning methods, discussing their relative merits and shortcomings in terms of accuracy and overall performance. Staking which is an ensemble machine learning approach emerges as the best approach for cervical cancer classification.