Current Search: Diagnosis -- Statistical methods (x)
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Title
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AUC estimation under various survival models.
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Creator
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Chang, Fazhe., Charles E. Schmidt College of Science, Department of Mathematical Sciences
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Abstract/Description
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In the medical science, the receiving operationg characteristic (ROC) curve is a graphical representation to evaluate the accuracy of a medical diagnostic test for any cut-off point. The area under the ROC curve (AUC) is an overall performance measure for a diagnostic test. There are two parts in this dissertation. In the first part, we study the properties of bi-Exponentiated Weibull models. FIrst, we derive a general moment formula for single Exponentiated Weibull models. Then we move on to...
Show moreIn the medical science, the receiving operationg characteristic (ROC) curve is a graphical representation to evaluate the accuracy of a medical diagnostic test for any cut-off point. The area under the ROC curve (AUC) is an overall performance measure for a diagnostic test. There are two parts in this dissertation. In the first part, we study the properties of bi-Exponentiated Weibull models. FIrst, we derive a general moment formula for single Exponentiated Weibull models. Then we move on to derive the precise formula of AUC and study the maximus likelihood estimation (MLE) of the AUC. Finally, we obtain the asymptotoc distribution of the estimated AUC. Simulation studies are used to check the performance of MLE of AUC under the moderate sample sizes. The second part fo the dissertation is to study the estimation of AUC under the crossing model, which extends the AUC formula in Gonen and Heller (2007).
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Date Issued
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2012
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PURL
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http://purl.flvc.org/FAU/3359287
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Subject Headings
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Receiver operating characteristic curves, Medical screening, Statistical methods, Diagnosis, Statistical methods, Smoothing (Statistics)
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Format
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Document (PDF)
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Title
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Using classification and regression tree to detect hematology abnormalities.
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Creator
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Qian, Cheng., Florida Atlantic University, Wu, Jie, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
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Abstract/Description
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The detection of the abnormal blood cells and particles in a blood test is essential in medical diagnosis. The detection rules, which are usually implemented in the widely used automated hematology analyzer, are therefore critical for the health and even lives of millions of people. The research endeavor of this thesis is on generating such detection rules using a supervised machine learning algorithm. The first part of this thesis studies the hematology data and surveys the popular...
Show moreThe detection of the abnormal blood cells and particles in a blood test is essential in medical diagnosis. The detection rules, which are usually implemented in the widely used automated hematology analyzer, are therefore critical for the health and even lives of millions of people. The research endeavor of this thesis is on generating such detection rules using a supervised machine learning algorithm. The first part of this thesis studies the hematology data and surveys the popular classification algorithms. In the second part, the selected algorithm, CART, is implemented with deliberately selected parameters. In the third part, a modification of the algorithm, logical pruning with Enclose the Normal principle, is exercised. To extend the algorithm and to achieve better performance, I developed and implemented the idea of decision tree combinations. The research has proven to be successful by the achievement of good performance and reasonable detection rules.
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Date Issued
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2004
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PURL
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http://purl.flvc.org/fcla/dt/13189
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Subject Headings
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Regression analysis, Health survey--Statistical methods, Medical statistics, Blood--Diseases--Diagnosis, Hematology, Blood--Examination
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Format
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Document (PDF)
