Feasibility of Predicting Rare Adverse Outcomes of Pregnancy Using Machine Learning: Can an Algorithm Use Fetal to Placental Weight Ratios to Predict Which Pregnancies End in Stillbirth? All Restricted

Nlandu, Mundayi (Spring 2023)

Permanent URL: https://etd.library.emory.edu/concern/etds/bk128c287?locale=it

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About this Master's Thesis

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Permission granted by the author to include this thesis or dissertation in this repository. All rights reserved by the author. Please contact the author for information regarding the reproduction and use of this thesis or dissertation.

School	Rollins School of Public Health
Department	Epidemiology
Degree	M.P.H.
Submission	Master's Thesis
Language	English
Research Field	Health Sciences, Public Health Health Sciences, Epidemiology
Parola chiave	Super Learner machine learning stillbirth receiver operating characteristic curve prediction estimated placental volume area under the curve fetal to placental weight ratio
Committee Chair / Thesis Advisor	Christiansen-Lindquist, Lauren, Emory University Naimi, Ashley, Emory University

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