Date of Award
2014
Rights
Access is available to all users
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
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Sykes, Conrad, "MINING MULTI-GRANULAR MULTIVARIATE MEDICAL MEASUREMENTS" (2014). EWU Masters Thesis Collection. 196.
https://dc.ewu.edu/theses/196
