Please use this identifier to cite or link to this item:
Title: ROC Analysis in Diagnostic Medicine
Keywords: ROC, diagnostic test, HUM, bootstrap, multiple-category classification, combining multiple markers
Issue Date: 31-Mar-2010
Citation: ZHANG YANYU (2010-03-31). ROC Analysis in Diagnostic Medicine. ScholarBank@NUS Repository.
Abstract: The Receiver Operating Characteristic (ROC) curve and Area Under the ROC Curve (AUC) are effective statistical tools for evaluating the accuracy of diagnostic tests for binary-class medical data. However, many real-world biomedical problems involve more than two categories. The Volume Under the ROC Surface (VUS) and Hypervolume Under the ROC Manifold (HUM) measures are extensions for AUC under three-class and multiple-class models. Inference methods for such measures have been proposed recently. In this thesis, we propose rigorous and automated approaches to sort the multiple categories for HUM by using simple summary statistics such as means. We also supply a general discussion on the minimum acceptable HUM values in multiple-category classification problems. Furthermore, we explore statistical methods of combining multiple tests for multiple-category classification to optimize the accuracy of the combined markers under the criteria of ROC measures. Our works in this thesis provide insights on screening among large number of tests in health science studies.
Appears in Collections:Ph.D Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
ZhangYanyu PH.D Thesis.pdf603.04 kBAdobe PDF



Page view(s)

checked on Apr 12, 2019


checked on Apr 12, 2019

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.