Engineering Commons^™

Utilizing Data Mining Techniques And Ensemble Learning To Predict Development Of Surgical Site Infections In Gynecologic Cancer Patients, John R. Mcdonough

Graduate Dissertations and Theses

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 …