{"id":{"repo_id":"duquesne","oai_identifier":"oai:dsc.duq.edu:etd-1410"},"canonical_url":"https://search.dev.ndltd.org/etd/duquesne/oai:dsc.duq.edu:etd-1410","repository":{"repo_id":"duquesne","name":"Duquesne","base_url":"https://dsc.duq.edu/do/oai/"},"display":{"title":"Classification Tree Models for Predicting Cancer Status","abstract":"Early detection of cancers might improve the clinical outcomes. Multiple biomarkers with a novel LabMAP technology were used as the laboratory method to develop the diagnostic assay for ovarian, breast, endometrial, and lung cancer. To evaluate the accuracy of early stage detection, logistic regression (with forward selection) and classification tree models were applied as statistical methods. Furthermore, complexity parameters and the number of bootstrap samples were varied to assess the effect on sensitivity and specificity. The receiver operating characteristic curves reflected high sensitivities and specificities.","abstract_html":"Early detection of cancers might improve the clinical outcomes. Multiple biomarkers with a novel LabMAP technology were used as the laboratory method to develop the diagnostic assay for ovarian, breast, endometrial, and lung cancer. To evaluate the accuracy of early stage detection, logistic regression (with forward selection) and classification tree models were applied as statistical methods. Furthermore, complexity parameters and the number of bootstrap samples were varied to assess the effect on sensitivity and specificity. The receiver operating characteristic curves reflected high sensitivities and specificities.","abstract_has_math":false,"creators":["Chen, Pu"],"institution":null,"degree_name":"MS","degree_level":"Immediate Access","degree_discipline":"Computational Mathematics","degree_department":null,"school":null,"contributors":["Doug Landsittel","Frank D'Amico","Mark Mazur"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2009,"date_issued":"2009-01-01T08:00:00Z","date_published":"2009-01-01T08:00:00Z","updated_at":"2026-07-24T02:09:27Z","subjects":["classification tree","cancer"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://dsc.duq.edu/etd/397","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Doug Landsittel","Frank D'Amico","Mark Mazur"]},{"key":"dc:creator","label":"Author","values":["Chen, Pu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2018-08-03T07:00:00Z"]},{"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":["classification tree","cancer"]}]},{"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/397"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Early detection of cancers might improve the clinical outcomes. Multiple biomarkers with a novel LabMAP technology were used as the laboratory method to develop the diagnostic assay for ovarian, breast, endometrial, and lung cancer. To evaluate the accuracy of early stage detection, logistic regression (with forward selection) and classification tree models were applied as statistical methods. Furthermore, complexity parameters and the number of bootstrap samples were varied to assess the effect on sensitivity and specificity. The receiver operating characteristic curves reflected high sensitivities and specificities."]},{"key":"dc:title","label":"Title","values":["Classification Tree Models for Predicting Cancer Status"]}]}],"canonical_facts":{"dc:contributor":["Doug Landsittel","Frank D'Amico","Mark Mazur"],"dc:creator":["Chen, Pu"],"dc:date.available":["2018-08-03T07:00:00Z"],"dc:description.abstract":["Early detection of cancers might improve the clinical outcomes. Multiple biomarkers with a novel LabMAP technology were used as the laboratory method to develop the diagnostic assay for ovarian, breast, endometrial, and lung cancer. To evaluate the accuracy of early stage detection, logistic regression (with forward selection) and classification tree models were applied as statistical methods. Furthermore, complexity parameters and the number of bootstrap samples were varied to assess the effect on sensitivity and specificity. The receiver operating characteristic curves reflected high sensitivities and specificities."],"dc:identifier":["https://dsc.duq.edu/etd/397"],"dc:language":["English"],"dc:subject":["classification tree","cancer"],"dc:title":["Classification Tree Models for Predicting Cancer Status"],"thesis:degree_discipline":["Computational Mathematics"],"thesis:degree_level":["Immediate Access"],"thesis:degree_name":["MS"]},"updated_at":"2026-07-24T02:09:27Z"}