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

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dc.contributor.advisor Grewal, Gary
dc.contributor.advisor Areibi, Shawki Sheikh-Nia, Samaneh 2012-09-04T13:49:21Z 2012-09-04T13:49:21Z 2012-07 2012-08-22 2012-09-04
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 Computer Science en_US Master of Science en_US Department of Computing and Information Science en_US
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