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Date of Award

2014

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

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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