Main content

An Investigation of Standard and Ensemble Based Classification Techniques for the Prediction of Hospitalization Duration

Show simple item record

dc.contributor.advisor Grewal, Gary
dc.contributor.advisor Areibi, Shawki
dc.contributor.author Sheikh-Nia, Samaneh
dc.date.accessioned 2012-09-04T13:49:21Z
dc.date.available 2012-09-04T13:49:21Z
dc.date.copyright 2012-07
dc.date.created 2012-08-22
dc.date.issued 2012-09-04
dc.identifier.uri http://hdl.handle.net/10214/3902
dc.description.abstract In any health-care system, early identification of individuals who are most at risk of developing an illness is vital, not only to ensure that a patient is provided with the appropriate treatment, but also to avoid the considerable costs associated with unnecessary hospitalization. To achieve this goal there is a need for a breakthrough prediction method that is capable of dealing with a real world medical data which is inherently complex. In this study, we show how standard classification algorithms can be employed collectively to predict the length of stay in a hospital of a patient in the upcoming year, based on their medical history. Multiple classifiers are used to perform the prediction task, since real world medical data is significantly complex making the classification task very challenging. The data is voluminous, consists of wide range of class values some of which with a few instances, and it is highly unbalanced making the classification of minority classes very difficult. We propose two Sequential Ensemble Classification (SEC) schemes, one based on an ensemble of homogeneous classifiers, and a second based on a heterogeneous ensemble of classifiers, in three hierarchical granularity levels. The goal of using this system is to provide increased performance over the standard classifiers. This method is highly beneficial when dealing with complex data which is multi-class and highly unbalanced. en_US
dc.language.iso en en_US
dc.subject Ensemble Based Classification en_US
dc.subject Decomposition Technique en_US
dc.subject Unbalanced Class Distribution en_US
dc.subject Multi-Class Unbalanced Problem en_US
dc.title An Investigation of Standard and Ensemble Based Classification Techniques for the Prediction of Hospitalization Duration en_US
dc.type Thesis en_US
dc.degree.programme Computer Science en_US
dc.degree.name Master of Science en_US
dc.degree.department Department of Computing and Information Science en_US
dc.rights.license All items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.


Files in this item

Files Size Format View
Sama_Thesis.pdf 4.745Mb PDF View/Open

This item appears in the following Collection(s)

Show simple item record