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Date of Award
2014
Rights
Access perpetually restricted to EWU users with an active EWU NetID
Document Type
Thesis: EWU Only
Degree Name
Master of Science (MS) in Computer Science
Department
Computer Science
First Advisor
Dr. Dan Li
Second Advisor
Dr. Carol Taylor
Third Advisor
Dr. Robin O'Quinn
Abstract
The purpose of this research is to study the performance of kNN (k Nearest Neighbor) classification approach to determine patients’ mortality rate using ICU (Intensive Care Unit) medical records. The ICU data contains medical records collected during the patients’ first 48 hours stay at the ICU. The challenge of this research is the processing of ICU multivariate and high dimensional time-series data collected at irregular time periods. To handle the ICU irregular multivariate time-series three different methods were developed: Capture Statistics, Detect Changes, and Aggregate Segments. We examine the effectiveness of each method on kNN classification. In addition, this paper addresses imbalanced class distributions and their effect on kNN performance.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Djulovic, Admir, "A study of kNN using ICU multivariate time series data" (2014). EWU Masters Thesis Collection. 262.
https://dc.ewu.edu/theses/262
