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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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Principais autores: Muhammad Thaqiyuddin Mizan, Ferda Ernawan, Shahreen Kasim, Aldo Erianda, Abdullah Munzir Mohd Fauzi
Format: Artigo
Sprog:Inglês
Udgivet: Society of Visual Informatics 2025-09-01
Serier:JOIV: International Journal on Informatics Visualization
Fag:
cervical cancer
imputation techniques
missing data
mice imputer
xgboost imputer
lightgbm imputer
Online adgang:https://joiv.org/index.php/joiv/article/view/4501
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