Improving Prediction of Cervical Cancer Using KNN Imputed SMOTE Features and Multi-Model Ensemble Learning Approach

SIMPLE SUMMARY: This paper presents a cervical cancer detection approach where the KNN Imputer techniques is used to fill the missing values and after that SMOTE upsampled features are utilized to train a multi-model ensemble learning approach. Results demonstrate that use of KNN Imputed SMOTE featu...

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Detalles Bibliográficos
Autores principales: Karamti, Hanen, Alharthi, Raed, Anizi, Amira Al, Alhebshi, Reemah M., Eshmawi, Ala’ Abdulmajid, Alsubai, Shtwai, Umer, Muhammad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10486648/
https://www.ncbi.nlm.nih.gov/pubmed/37686692
http://dx.doi.org/10.3390/cancers15174412

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10486648/
https://www.ncbi.nlm.nih.gov/pubmed/37686692
http://dx.doi.org/10.3390/cancers15174412

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