Impact of Data Pre-Processing Techniques on XGBoost Model Performance for Predicting All-Cause Readmission and Mortality Among Patients with Heart Failure

Impact of Data Pre-Processing Techniques on XGBoost Model Performance for Predicting All-Cause Readmission and Mortality Among Patients with Heart Failure

<b>Background:</b> Heart failure poses a significant global health challenge, with high rates of readmission and mortality. Accurate models to predict these outcomes are essential for effective patient management. This study investigates the impact of data pre-processing techniques on XG...

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Bibliographic Details
Main Authors: Qisthi Alhazmi Hidayaturrohman, Eisuke Hanada
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:BioMedInformatics
Subjects:
heart failure
XGBoost
data pre-processing
imputation
predictive analytics
standardization
Online Access:https://www.mdpi.com/2673-7426/4/4/118
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