Defense Date
5-14-2009
Graduation Date
2009
Availability
Immediate Access
Submission Type
thesis
Degree Name
MS
Department
Computational Mathematics
School
McAnulty College and Graduate School of Liberal Arts
Committee Chair
Doug Landsittel
Committee Member
Frank D'Amico
Committee Member
Mark Mazur
Keywords
classification tree, cancer
Abstract
Early detection of cancers might improve the clinical outcomes. Multiple biomarkers with a novel LabMAP technology were used as the laboratory method to develop the diagnostic assay for ovarian, breast, endometrial, and lung cancer. To evaluate the accuracy of early stage detection, logistic regression (with forward selection) and classification tree models were applied as statistical methods. Furthermore, complexity parameters and the number of bootstrap samples were varied to assess the effect on sensitivity and specificity. The receiver operating characteristic curves reflected high sensitivities and specificities.
Format
Language
English
Recommended Citation
Chen, P. (2009). Classification Tree Models for Predicting Cancer Status (Master's thesis, Duquesne University). Retrieved from https://dsc.duq.edu/etd/397