Date of Award

5-2021

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Mathematical Sciences

First Advisor

Munevver Mine Subasi

Second Advisor

Joo Young Park

Third Advisor

Ryan White

Fourth Advisor

Susan Serrano

Abstract

This thesis integrates fundamental concepts from conventional statistics with the more explanatory, algorithmic, and computational techniques offered by machine learning to predict early mortality risk of surgical patients. Well-known classification methods, including Random Forest, Decision Trees, Nearest Neighbor, Stochastic Gradient Descent, Logistic Regression, Na¨ıve Bayes, Bayes Network, Neural Networks, and Support Vector Machines, are utilized to predict mortality risk of elective general surgical patients treated between January 2005 and September 2010 at the Cleveland Clinic [33]. Clinical factors include surgery type, age, gender, race, BMI, underlying chronic conditions, surgical risk indices, surgical timing predictors, the 30-day mortality, and in-hospital complication for each patient. 10×10-folding cross validation experiments are conducted to evaluate the prediction performance on low, medium, and high mortality risk groups. A Decision Tree classification model consisting of 83 low and 135 high risk patterns is presented. The overall average accuracy of the classifiers applied to predict low and high risk mortality is 85.2% with precision of 0.89, recall of 0.95, and F-measure of 0.92. The overall accuracy of the classifiers applied to predict low, medium, and high risk mortality is 84.7% with precision of 0.89, recall of 0.94, and F-measure of 0.91.

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