{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/318"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/318","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Data mining occurrences of infectious diseases with SNOMED CT","abstract":"Synonyms within SNOMED CT’s structure give meaning to the clinical terminology. The hypothesis in this thesis is that the number of synonyms of a disease within SNOMED CT can be used to predict the number of occurrences of an infectious disease reported on by the World Health Organization (WHO). Using simple Classification and Regression (CART), Bayes theory, and Best Fit trees, prediction algorithms are created based on the number of synonyms in infectious disease terms of SNOMED CT, the number of those diseases world-wide, the region of occurrence of the disease, and the year of occurrence of the disease. The results of experiments predict the number of occurrences of a disease correctly 67% of the time by using Simple Cart method; Bayes and Best Fit Trees each produce the correct number of occurrences 61% of the time.","abstract_html":"Synonyms within SNOMED CT’s structure give meaning to the clinical terminology. The hypothesis in this thesis is that the number of synonyms of a disease within SNOMED CT can be used to predict the number of occurrences of an infectious disease reported on by the World Health Organization (WHO). Using simple Classification and Regression (CART), Bayes theory, and Best Fit trees, prediction algorithms are created based on the number of synonyms in infectious disease terms of SNOMED CT, the number of those diseases world-wide, the region of occurrence of the disease, and the year of occurrence of the disease. The results of experiments predict the number of occurrences of a disease correctly 67% of the time by using Simple Cart method; Bayes and Best Fit Trees each produce the correct number of occurrences 61% of the time.","abstract_has_math":false,"creators":["Ciolko, Ewelina"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Health Sciences (MHSc)","degree_level":null,"degree_discipline":"Health Informatics","degree_department":null,"school":null,"contributors":[],"advisors":["Lu, Fletcher"],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-05-01","date_published":"2013-05-01","updated_at":"2026-07-24T05:35:22Z","subjects":["SNOMED CT","Data mining","World Health Organization","Infectious diseases","Simple CART theory","Naive Bayes","Best Fit Trees","World health statistics"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/318","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Lu, Fletcher"]},{"key":"dc:creator","label":"Author","values":["Ciolko, Ewelina"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2013-07-08T16:47:54Z","2022-03-29T16:53:50Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2013-07-08T16:47:54Z","2022-03-29T16:53:50Z"]},{"key":"dc:date.issued","label":"Date","values":["2013-05-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Health Informatics"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Health Sciences (MHSc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["SNOMED CT","Data mining","World Health Organization","Infectious diseases","Simple CART theory","Naive Bayes","Best Fit Trees","World health statistics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/318"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Synonyms within SNOMED CT’s structure give meaning to the clinical terminology. The hypothesis in this thesis is that the number of synonyms of a disease within SNOMED CT can be used to predict the number of occurrences of an infectious disease reported on by the World Health Organization (WHO). Using simple Classification and Regression (CART), Bayes theory, and Best Fit trees, prediction algorithms are created based on the number of synonyms in infectious disease terms of SNOMED CT, the number of those diseases world-wide, the region of occurrence of the disease, and the year of occurrence of the disease. The results of experiments predict the number of occurrences of a disease correctly 67% of the time by using Simple Cart method; Bayes and Best Fit Trees each produce the correct number of occurrences 61% of the time."]},{"key":"dc:title","label":"Title","values":["Data mining occurrences of infectious diseases with SNOMED CT"]}]}],"canonical_facts":{"dc:contributor.advisor":["Lu, Fletcher"],"dc:creator":["Ciolko, Ewelina"],"dc:date.accessioned":["2013-07-08T16:47:54Z","2022-03-29T16:53:50Z"],"dc:date.available":["2013-07-08T16:47:54Z","2022-03-29T16:53:50Z"],"dc:date.issued":["2013-05-01"],"dc:description.abstract":["Synonyms within SNOMED CT’s structure give meaning to the clinical terminology. The hypothesis in this thesis is that the number of synonyms of a disease within SNOMED CT can be used to predict the number of occurrences of an infectious disease reported on by the World Health Organization (WHO). Using simple Classification and Regression (CART), Bayes theory, and Best Fit trees, prediction algorithms are created based on the number of synonyms in infectious disease terms of SNOMED CT, the number of those diseases world-wide, the region of occurrence of the disease, and the year of occurrence of the disease. The results of experiments predict the number of occurrences of a disease correctly 67% of the time by using Simple Cart method; Bayes and Best Fit Trees each produce the correct number of occurrences 61% of the time."],"dc:identifier.uri":["https://hdl.handle.net/10155/318"],"dc:language.iso":["en"],"dc:subject":["SNOMED CT","Data mining","World Health Organization","Infectious diseases","Simple CART theory","Naive Bayes","Best Fit Trees","World health statistics"],"dc:title":["Data mining occurrences of infectious diseases with SNOMED CT"],"dc:type":["Thesis"],"thesis:degree_discipline":["Health Informatics"],"thesis:degree_name":["Master of Health Sciences (MHSc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:22Z"}