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Utilizing Data Mining Techniques and...

McDonough, John R.

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Utilizing Data Mining Techniques and Ensemble Learning to Predict Development of Surgical Site Infections in Gynecologic Cancer Patients.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Utilizing Data Mining Techniques and Ensemble Learning to Predict Development of Surgical Site Infections in Gynecologic Cancer Patients./
作者:	McDonough, John R.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	103 p.
附註:	Source: Masters Abstracts International, Volume: 79-11.
Contained By:	Masters Abstracts International79-11.
標題:	Industrial engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10687519
ISBN:	9780355934519

Utilizing Data Mining Techniques and Ensemble Learning to Predict Development of Surgical Site Infections in Gynecologic Cancer Patients.
McDonough, John R.

Utilizing Data Mining Techniques and Ensemble Learning to Predict Development of Surgical Site Infections in Gynecologic Cancer Patients. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 103 p.

Source: Masters Abstracts International, Volume: 79-11.

Thesis (M.Eng.)--State University of New York at Binghamton, 2018.

This item must not be added to any third party search indexes.

Surgical site infections are costly to both patients and hospitals, increase patient mortality, and are the most common form of a hospital acquired infection. Gynecological cancer surgery patients are already at higher risk of developing an infection due to the suppression of their immune system. This research leverages popular data mining techniques to create a prediction model to identify high risk patients. Implemented techniques include logistic regression, naive Bayes, recursive partitioning and regression trees, random forest, feed forward neural network, k-nearest neighbor, and support vector machines with linear kernel. Weighted stacked generalization was implemented to improve upon the individual base level model's performance. The chosen meta level classifiers were support vector machines with linear kernel, logistic regression, and k-nearest neighbor. The result is a model that identifies high-risk patients immediately following a surgical procedure with an AUC of 0.6864, accuracy of 0.6744, sensitivity of 0.7, and specificity of 0.6728.

ISBN: 9780355934519Subjects--Topical Terms:

526216
Industrial engineering.

Utilizing Data Mining Techniques and Ensemble Learning to Predict Development of Surgical Site Infections in Gynecologic Cancer Patients.
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