Please use this identifier to cite or link to this item: https://zone.biblio.laurentian.ca/handle/10219/2263
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dc.contributor.authorAlenezi, Anwar-
dc.date.accessioned2014-10-08T13:56:52Z-
dc.date.available2014-10-08T13:56:52Z-
dc.date.issued2014-10-08-
dc.identifier.urihttps://zone.biblio.laurentian.ca/dspace/handle/10219/2263-
dc.description.abstractData have been obtained from King Khaled General Hospital in Saudi Arabia. In this project, I am trying to discover patterns in these data by using implemented algorithms in an experimental tool, called Rough Set Graphic User Interface (RSGUI). Several algorithms are available in RSGUI, each of which is based in Rough Set theory. My objective is to find short meaningful predictive rules. First, we need to find a minimum set of attributes that fully characterize the data. Some of the rules generated from this minimum set will be obvious, and therefore uninteresting. Others will be surprising, and therefore interesting. Usual measures of strength of a rule, such as length of the rule, certainty and coverage were considered. In addition, a measure of interestingness of the rules has been developed based on questionnaires administered to human subjects. There were bugs in the RSGUI java codes and one algorithm in particular, Inductive Learning Algorithm (ILA) missed some cases that were subsequently resolved in ILA2 but not updated in RSGUI. I solved the ILA issue on RSGUI. So now ILA on RSGUI is running well and gives good results for all cases encountered in the hospital administration and student records data.en_CA
dc.language.isoenen_CA
dc.publisherLaurentian University of Sudburyen_CA
dc.subjectRough Set Graphic User Interfaceen_CA
dc.subjectRough Set theoryen_CA
dc.subjecthealth analyticsen_CA
dc.subjectalgorithmen_CA
dc.subjectpredictive rulesen_CA
dc.titleFinding patterns in student and medical office data using rough setsen_CA
dc.typeThesisen_CA
dc.description.degreeMaster's Thesesen_CA
dc.publisher.grantorLaurentian University of Sudburyen_CA
Appears in Collections:Master's theses
Master's Theses

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