Data mining occurrences of infectious diseases with SNOMED CT

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.

Name thesis:degree_name

Master of Health Sciences (MHSc)

Discipline thesis:degree_discipline

Health Informatics

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2013

Author dc:creator

• Ciolko, Ewelina

Advisor dc:contributor.advisor

• Lu, Fletcher

• SNOMED CT
• Data mining
• World Health Organization
• Infectious diseases
• Simple CART theory
• Naive Bayes
• Best Fit Trees
• World health statistics

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/318

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/318