Cervical cancer detection using K nearest neighbor imputer and stacked ensemble learningmodel.

ABSTRACT:

Objective

Cervical cancer stands as a leading cause of mortality among women in developing nations. To ensure the reduction of its adverse consequences, the primary protocols to be adhered to involve early detection and treatment under the guidance of expert medical professionals. An effective approach for identifying this form of malignancy involves the examination of Pap smear images. However, in the context of automating cervical cancer detection, many of the existing datasets frequently exhibit missing data points, a factor that can substantially impact the effectiveness of machine learning models.

Methods

In response to these hurdles, this research introduces an automated system designed to predict cervical cancer with a dual focus: adeptly managing missing data while a

SUBMITTER: Chen X 

PROVIDER: S-EPMC10548812 | biostudies-literature | 2023 Jan-Dec

REPOSITORIES: biostudies-literature