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|Title:||COMPARISONS BETWEEN ALGORITHMS TO CONSTRUCT THE RECEIVER OPERATING CHARACTERISTIC (ROC) CURVE FOR MULTIPLE SCREENING/DIAGNOSTIC TESTS|
|Advisor:||Walter, Stephen D.|
|Keywords:||Statistics and Probability;Statistics and Probability|
|Abstract:||<p>Cancer can be an extremely aggressive disease, with a poor survival rate among the patients that are in the advanced stages of the disease. Early intervention can significantly affect the outcome of the disease. The requirement of early intervention necessitates a reliable cancer screening. Regular use of screening, followed by timely treatment when cancer is diagnosed, can help decrease the chances of occurrence of death due to cancer. There are several tests available for the early detection and diagnosis of cancer. When multiple diagnostic tests are performed on an individual or multiple disease markers are available it may be possible to combine the information to diagnose disease. By combining multiple tests we can optimize diagnostic accuracy. The combination of ultrasound and mammography as markers for cancer diagnosis could be useful for early intervention. Selecting a statistical tool capable of assessing the performance of a combination of different diagnostic tests is important in selecting the most suitable diagnostic standard. One way of determining the performance of any combination of diagnostic tests is through the use of the receiver operating characteristic (ROC) curve. Baker (2000) proposed three ranking algorithms that optimize the ROC curve. The objective of this study was to develop and select the ranking algorithm which provides the optimal area under the ROC curve to differentiate cancer from benign. Statistically, unordered algorithms proved to be the best among the three algorithms giving average AUCs of 0.96510, followed by Jagged Ordered Algorithm and Rectangular Ordered Algorithm giving average AUCs of 0.96396 and 0.94314 respectively. Clinically, ordered algorithms seem to be the better choice due to their convenience.</p>|
|Appears in Collections:||Open Access Dissertations and Theses|
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