The Effect of Data Missingness on Machine Learning Predictions of Uncontrolled Diabetes Using All of Us Data

The Effect of Data Missingness on Machine Learning Predictions of Uncontrolled Diabetes Using All of Us Data

Electronic Health Records (EHR) provide a vast amount of patient data that are relevant to predicting clinical outcomes. The inherent presence of missing values poses challenges to building performant machine learning models. This paper aims to investigate the effect of various imputation methods on...

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Bibliographic Details
Main Authors: Zain Jabbar, Peter Washington
Format: Article
Language:English
Published: MDPI AG 2024-03-01
Series:BioMedInformatics
Subjects:
algorithmic fairness
electronic health records
data missingness
data imputation
diabetes
Online Access:https://www.mdpi.com/2673-7426/4/1/43

Internet

https://www.mdpi.com/2673-7426/4/1/43

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