Designing Reliable Machine Learning Algorithms for Early Prediction of Preeclampsia

Bennett, Rachel

Designing Reliable Machine Learning Algorithms for Early Prediction of Preeclampsia

dc.contributor.advisor	Razzaghi, Talayeh
dc.contributor.author	Bennett, Rachel
dc.contributor.committeeMember	Hougen, Dean
dc.contributor.committeeMember	Nicholson, Charles
dc.date.accessioned	2021-08-06T13:43:59Z
dc.date.available	2021-08-06T13:43:59Z
dc.date.issued	2021-08
dc.date.manuscript	2021-08
dc.description.abstract	Known as a pregnancy complication due to high blood pressure and may be accompanied by damage to another organ system, preeclampsia afflicts between 3 and 6 percent of US pregnancies each year. Studies have shown the importance of early detection of preeclampsia to prevent further complications that are detrimental to both mother and infant. In this work, we develop an algorithmic modification of Deep Neural Networks to identify high risk patients in preeclampsia diagnosis using imbalanced datasets in the presence of missing values. We identify the most influential set of clinical features relevant to preeclampsia and train a classifier that can be embedded within a clinical decision support system. Our results provide evidence in favor of increased consideration of patient race/ethnicity in preeclampsia prediction, and for more personalized medicine in general.	en_US
dc.identifier.uri	https://hdl.handle.net/11244/330235
dc.language	en_US	en_US
dc.rights	Attribution-ShareAlike 4.0 International	*
dc.rights.uri	http://creativecommons.org/licenses/by-sa/4.0/	*
dc.subject	Data Science and Analytics	en_US
dc.thesis.degree	Master of Science	en_US
dc.title	Designing Reliable Machine Learning Algorithms for Early Prediction of Preeclampsia	en_US
ou.group	Gallogly College of Engineering	en_US
shareok.orcid	https://orcid.org/0000-0001-6097-4809	en_US