Date of Award

2014

Document Type

Thesis

Degree Name

Master of Science (MS) in Computer Science

Department

Computer Science

Abstract

This thesis is motivated by the need to predict the mortality of patients in the Intensive Care Unit. The heart of this problem revolves around being able to accurately classify multivariate, multi-granular time series patient data. The approach ultimately taken in this thesis involves using Z-Score normalization to make variables comparable, Single Value Decomposition to reduce the number of features, and a Support Vector Machine to classify patient tuples. This approach proves to outperform other classification models such as k-Nearest Neighbor and demonstrates that SVM is a viable model for this project. The hope is that going forward other work can build off of this research and one day make an impact in the medical community.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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