Novel ensemble learning approach with SVM-imputed ADASYN features for enhanced cervical cancer prediction.

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 presents an automated system adept at managing missing information using ADASYN characteristics, resulting in exceptional accuracy. The proposed methodology integrates a voting classifier model harnessing the predictive capacity of three distinct machine learning models. It further incorporates SVM Imputer and ADASYN up-sampled features to mitigate miss

SUBMITTER: Munshi RM 

PROVIDER: S-EPMC10781159 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature