Classification Tree Models for Predicting Cancer Status

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.

Name thesis:degree_name

MS

Level thesis:degree_level

Immediate Access

Discipline thesis:degree_discipline

Computational Mathematics

Year dc:date.available

2009

Author dc:creator

• Chen, Pu

Contributors dc:contributor

• Doug Landsittel
• Frank D'Amico
• Mark Mazur

• classification tree
• cancer

Language dc:language

English

Repository record dc:identifier

https://dsc.duq.edu/etd/397

OAI identifier oai:identifier

oai:dsc.duq.edu:etd-1410