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>

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Rawan Abulibdeh (19206115) (author)
مؤلفون آخرون: Karen Tu (39526) (author), Debra A. Butt (11268344) (author), Anthony Train (20577186) (author), Noah Crampton (11268353) (author), Ervin Sejdić (284825) (author)
منشور في: 2025
الموضوعات:
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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الوصف
الملخص:<p>The best-performing algorithm is indicated in bold for each metric.</p>

مواد مشابهة