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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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Autori principali: Muhammad Thaqiyuddin Mizan, Ferda Ernawan, Shahreen Kasim, Aldo Erianda, Abdullah Munzir Mohd Fauzi
Natura: Artigo
Lingua:Inglês
Pubblicazione: Society of Visual Informatics 2025-09-01
Serie:JOIV: International Journal on Informatics Visualization
Soggetti:
cervical cancer
imputation techniques
missing data
mice imputer
xgboost imputer
lightgbm imputer
Accesso online:https://joiv.org/index.php/joiv/article/view/4501
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