Evaluation metric values of the logistic regression model (LR), random forest classifier (RF), and the rule-based algorithm (RB) for each of the sociodemographic characteristics.

Evaluation metric values of the logistic regression model (LR), random forest classifier (RF), and the rule-based algorithm (RB) for each of the sociodemographic characteristics.

<p>The best-performing algorithm is indicated in bold for each metric.</p>

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Bibliographic Details
Main Author:	Rawan Abulibdeh (19206115) (author)
Other Authors:	Karen Tu (39526) (author), Debra A. Butt (11268344) (author), Anthony Train (20577186) (author), Noah Crampton (11268353) (author), Ervin Sejdić (284825) (author)
Published:	2025
Subjects:	Medicine Biotechnology Sociology Science Policy Biological Sciences not elsewhere classified vendor type demonstrated produce representative cohorts potentially vulnerable populations >&# 967 ;</ electronic medical records canadian medical graduate perform focused research document sociodemographic factors improve completeness rates significantly higher compared lowest documentation rates div >< p 2 </ sup medical research completeness rates >< sup significantly associated p </ documentation rates sociodemographic information sociodemographic data sociodemographic characteristics years since vendors (< various clinics significant variation missingness rate highest variation healthcare systems healthcare setting growing need gender identity foreign vs findings suggest descriptive statistics crucial need correlation analysis clinics indicated citizenship status adult patients 8 %) 7 %). 2 %) 05 ).
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