Improving prediction of cervical cancer using KNN imputer and multi-model ensemble learning.

ABSTRACT: Cervical cancer is a leading cause of women's mortality, emphasizing the need for early diagnosis and effective treatment. In line with the imperative of early intervention, the automated identification of cervical cancer has emerged as a promising avenue, leveraging machine learning techniques to enhance both the speed and accuracy of diagnosis. However, an inherent challenge in the development of these automated systems is the presence of missing values in the datasets commonly used for cervical cancer detection. Missing data can significantly impact the performance of machine learning models, potentially leading to inaccurate or unreliable results. This study addresses a critical challenge in automated cervical cancer identification-handling missing data in datasets. The study present a

SUBMITTER: Aljrees T 

PROVIDER: S-EPMC10763959 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature