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Using classification and regression tree to detect hematology abnormalities

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Date Issued:
2004
Summary:
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 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.
Title: Using classification and regression tree to detect hematology abnormalities.
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Name(s): Qian, Cheng.
Florida Atlantic University, Degree grantor
Wu, Jie, Thesis advisor
College of Engineering and Computer Science
Department of Computer and Electrical Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: monographic
Date Issued: 2004
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 94 p.
Language(s): English
Summary: 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 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.
Identifier: 9780496084579 (isbn), 13189 (digitool), FADT13189 (IID), fau:10047 (fedora)
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): College of Engineering and Computer Science
Thesis (M.S.)--Florida Atlantic University, 2004.
Subject(s): Regression analysis
Health survey--Statistical methods
Medical statistics
Blood--Diseases--Diagnosis
Hematology
Blood--Examination
Held by: Florida Atlantic University Libraries
Persistent Link to This Record: http://purl.flvc.org/fcla/dt/13189
Sublocation: Digital Library
Use and Reproduction: Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.