Premature birth has been identified as the single greatest cause of death worldwide in children under the age of five. This thesis will implement binary logistic regression and proportional odds ordinal logistic regression to predict different levels of premature birth and identify associated risk factors. The models will be built from the Center for Disease Control and Prevention's 2014 Vital Statistics Natality Birth Data containing nearly 4 million live births within the United States. Odds ratios and confidence intervals on risk factors were produced utilizing binary logistic regression.
","abstract_html":"<p>Premature birth has been identified as the single greatest cause of death worldwide in children under the age of five. This thesis will implement binary logistic regression and proportional odds ordinal logistic regression to predict different levels of premature birth and identify associated risk factors. The models will be built from the Center for Disease Control and Prevention's 2014 Vital Statistics Natality Birth Data containing nearly 4 million live births within the United States. Odds ratios and confidence intervals on risk factors were produced utilizing binary logistic regression.</p>","abstract_has_math":false,"creators":["Elwood, Clayton"],"institution":null,"degree_name":"MS","degree_level":"Immediate Access","degree_discipline":"Computational Mathematics","degree_department":null,"school":null,"contributors":["Dr. Frank D'Amico","Dr. John Kern","Dr. Stacy Levine"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-08-10T07:00:00Z","date_published":"2019-08-10T07:00:00Z","updated_at":"2026-07-24T02:10:50Z","subjects":["Logistic Regression","Ordinal Regression","Clinical Research","Premature Birth","Proportional Odds Model","Vital Statistics Natality Birth Data","NCHS","Applied Statistics","Biostatistics","Multivariate Analysis","Statistical Models","Vital and Health Statistics"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://dsc.duq.edu/etd/1803","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Frank D'Amico","Dr. John Kern","Dr. Stacy Levine"]},{"key":"dc:creator","label":"Author","values":["Elwood, Clayton"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2019-08-10T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computational Mathematics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Immediate Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Logistic Regression","Ordinal Regression","Clinical Research","Premature Birth","Proportional Odds Model","Vital Statistics Natality Birth Data","NCHS","Applied Statistics","Biostatistics","Multivariate Analysis","Statistical Models","Vital and Health Statistics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://dsc.duq.edu/etd/1803"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Premature birth has been identified as the single greatest cause of death worldwide in children under the age of five. This thesis will implement binary logistic regression and proportional odds ordinal logistic regression to predict different levels of premature birth and identify associated risk factors. The models will be built from the Center for Disease Control and Prevention's 2014 Vital Statistics Natality Birth Data containing nearly 4 million live births within the United States. Odds ratios and confidence intervals on risk factors were produced utilizing binary logistic regression.
"]},{"key":"dc:title","label":"Title","values":["Identifying Risk Factors Related to Premature Birth Through Binary Logistic and Proportional Odds Ordinal Logistic Regression"]}]}],"canonical_facts":{"dc:contributor":["Dr. Frank D'Amico","Dr. John Kern","Dr. Stacy Levine"],"dc:creator":["Elwood, Clayton"],"dc:date.available":["2019-08-10T07:00:00Z"],"dc:description.abstract":["Premature birth has been identified as the single greatest cause of death worldwide in children under the age of five. This thesis will implement binary logistic regression and proportional odds ordinal logistic regression to predict different levels of premature birth and identify associated risk factors. The models will be built from the Center for Disease Control and Prevention's 2014 Vital Statistics Natality Birth Data containing nearly 4 million live births within the United States. Odds ratios and confidence intervals on risk factors were produced utilizing binary logistic regression.
"],"dc:identifier":["https://dsc.duq.edu/etd/1803"],"dc:language":["English"],"dc:subject":["Logistic Regression","Ordinal Regression","Clinical Research","Premature Birth","Proportional Odds Model","Vital Statistics Natality Birth Data","NCHS","Applied Statistics","Biostatistics","Multivariate Analysis","Statistical Models","Vital and Health Statistics"],"dc:title":["Identifying Risk Factors Related to Premature Birth Through Binary Logistic and Proportional Odds Ordinal Logistic Regression"],"thesis:degree_discipline":["Computational Mathematics"],"thesis:degree_level":["Immediate Access"],"thesis:degree_name":["MS"]},"updated_at":"2026-07-24T02:10:50Z"}