Enhanced Precision in Cervical Cancer Diagnosis: A Multi-Stage Machine Learning Fusion Approach

Abstract: Cervical cancer remains a leading cause of female mortality, particularly in developing regions, underscoring the critical need for early detection and intervention guided by skilled medical professionals. While Pap smear images serve as valuable diagnostic tools, many available datasets for automated cervical cancer detection contain missing data, posing challenges for machine learning models' efficacy. To address these hurdles, this study pres- ents an automated system adept at managing missing information using ADASYN charac- teristics, resulting in exceptional accuracy. The proposed methodology integrates a voting classifier model harnessing the predictive capacity of three distinct machine learning mod- els. It further incorporates SVM Imputer and ADASYN up-sampled features to mitigate miss- ing value concerns, while leveraging CNN-generated features to augment the model's capabilities. Notably, this model achieves remarkable performance metrics, boasting a 99.99% accuracy, precision, recall, and F1 score. A comprehensive comparative analysis evaluates the proposed model against various machine learning algorithms across four sce- narios: original dataset usage, SVM imputation, ADASYN feature utilization, and CNN-gen- erated features. Results indicate the superior efficacy of the proposed model over existing state-of-the-art techniques. This research not only introduces a novel approach but also offers actionable suggestions for refining automated cervical cancer detection systems. Its impact extends to benefiting medical practitioners by enabling earlier detection and improved patient care. Furthermore, the study's findings have substantial societal implica- tions, potentially reducing the burden of cervical cancer through enhanced diagnostic accu- racy and timely intervention.