Measuring MERCI: exploring data mining techniques for examining surgical outcomes of stroke patients

Mechanical Embolus Removal in Cerebral Ischemia (MERCI) has been supported by medical trials as an improved method of treating ischemic stroke past the safe window of time for administering clot-busting drugs, and was released for medical use in 2004. The importance of analyzing real-world data collected from MERCI clinical trials is key to providing insights on the effectiveness of MERCI. Most of the existing data analysis on MERCI results has thus far employed conventional statistical analysis techniques. To the best of the knowledge acquired in preliminary research, advanced data analytics and data mining techniques have not yet been systematically applied. To address the issue in this thesis, a comprehensive study on employing state of the art machine learning algorithms was conducted to generate prediction criteria for the outcome of MERCI patients. Specifically, the issue of how to choose the most significant attributes of a data set with limited instance examples was investigated. A few search algorithms to identify the significant attributes of the data set are proposed, followed by a performance analysis for each algorithm. Finally, this approach is applied to the real-world medical data provided by Southeast Regional Stroke Center at Erlanger Hospital of Chattanooga, Tennessee. Our experimental results have demonstrated that our proposed approach performs well.

Grantor dc:publisher

University of Tennessee at Chattanooga

Author dc:creator

• McNabb, Matthew Ronald

Contributors dc:contributor

• Cao, Yu
• Dumas, Joseph; Thompson, Jack
• College of Engineering and Computer Science

• Computational intelligence
• Data mining

Licence

• https://rightsstatements.org/page/InC/1.0/?language=en

Language dc:language

English, eng

Repository record dc:identifier

https://scholar.utc.edu/theses/54

OAI identifier oai:identifier

oai:scholar.utc.edu:theses-1053