Comparative Analysis of Robust Imputation Techniques for Enhancing Cervical Cancer Prediction with Missing Data

Handling missing data is a critical challenge in machine learning applications, as it can significantly affect the accuracy and reliability of predictive models. Addressing this issue is crucial for developing robust systems that can deliver high-performance results. This study provides a comparativ...

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Autores principales:	Muhammad Thaqiyuddin Mizan, Ferda Ernawan, Shahreen Kasim, Aldo Erianda, Abdullah Munzir Mohd Fauzi
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	Society of Visual Informatics 2025-09-01
Colección:	JOIV: International Journal on Informatics Visualization
Materias:	cervical cancer imputation techniques missing data mice imputer xgboost imputer lightgbm imputer
Acceso en línea:	https://joiv.org/index.php/joiv/article/view/4501
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