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Comparison of artificial neural network and logistic regression models for prediction of diabetes type II with complications
Descriptive
TitleInfo
Title
Comparison of artificial neural network and logistic regression models for prediction of diabetes type II with complications
Name
(type = personal)
NamePart
(type = family)
Alshammari
NamePart
(type = given)
Muteb Hamed Saleh
NamePart
(type = date)
1984-
DisplayForm
Muteb Hamed Saleh Alshammari
Role
RoleTerm
(authority = RULIB)
author
Name
(type = personal)
NamePart
(type = family)
Coffman
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(type = given)
Frederick
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Frederick Coffman
Affiliation
Advisory Committee
Role
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(authority = RULIB)
chair
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(type = personal)
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(type = family)
Haque
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(type = given)
Syed
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Syed Haque
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Advisory Committee
Role
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(authority = RULIB)
internal member
Name
(type = personal)
NamePart
(type = family)
Srinivasan
NamePart
(type = given)
Shankar
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Shankar Srinivasan
Affiliation
Advisory Committee
Role
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(authority = RULIB)
internal member
Name
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NamePart
Rutgers University
Role
RoleTerm
(authority = RULIB)
degree grantor
Name
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NamePart
School of Health Professions
Role
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school
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Text
Genre
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theses
OriginInfo
DateCreated
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2016
DateOther
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(type = degree)
2016-10
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2016
Place
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xx
Language
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(type = code)
eng
Abstract
(type = abstract)
Type II diabetes mellitus (T2DM) is a growing health concern in the United States, affecting almost 30 million individuals, and currently ranking as the 7th leading cause of mortality. In addition, T2DM is associated with multi-systemic complications that contribute to both early mortality and decreased quality of life. Individuals with T2DM can be diagnosed by blood glucose tests, and previous studies have demonstrated increased risk factors for T2DM development, including obesity, particular ethnicities, personal history of polycystic ovary disease, or a family history of T2DM. The present study aimed to find the connection between T2DM complications including ketoacidosis, hyperosmolarity, renal manifestations, ophthalmic manifestations, neurological manifestations, and peripheral circulatory diseases with the most widespread risk factors, including gender, race, family history of diabetes, obesity, smoking, alcohol-related disorders, hyperlipidemia, hypertension, hypercholesterolemia, asthma, Vitamin D deficiency, and age. The strongest association was found between increasing age and peripheral circulatory disorders, with those over 65 years showing the highest correlation (OR=22.081). Strong connections were also found between Asian/Pacific Islanders and age >65 with renal complications, as well as between alcohol abuse and hyperglyceridemia with ketoacidosis (OR=3.303 and 2.992 respectively). This study also tested two predictive models, Logistic Regression and Neural Network (ANN), in modeling T2DM with complications. Classification methods tests showed that three complications – renal manifestations, neurological manifestations, and ketoacidosis – were better predicted by these models than the other complications, and that both models performed very similarly in both sensitivity and specificity. This study demonstrates that specific combinations of risk factors can predict increased probabilities of specific complications in T2DM patients, and that a neural network analysis model can predict these relationships as accurately and with the same sensitivity as a standard linear regression model.
Subject
(authority = RUETD)
Topic
Biomedical Informatics
Subject
(authority = ETD-LCSH)
Topic
Diabetes
Subject
(authority = ETD-LCSH)
Topic
Non-insulin-dependent diabetes
Subject
(authority = ETD-LCSH)
Topic
Neural networks (Computer science)
RelatedItem
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
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ETD
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(type = host)
TitleInfo
Title
School of Health Professions ETD Collection
Identifier
(type = local)
rucore10007400001
Identifier
ETD_7460
Identifier
(type = doi)
doi:10.7282/T3GT5QGK
PhysicalDescription
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electronic resource
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application/pdf
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text/xml
Extent
1 online resource (xv, 214 p. : ill.)
Note
(type = degree)
Ph.D.
Note
(type = bibliography)
Includes bibliographical references
Note
(type = statement of responsibility)
by Muteb Hamed Saleh Alshammari
Location
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(displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Genre
(authority = ExL-Esploro)
ETD doctoral
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Rights
RightsDeclaration
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The author owns the copyright to this work.
RightsHolder
(type = personal)
Name
FamilyName
Alshammari
GivenName
Muteb
MiddleName
Hamed Saleh
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
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2016-07-30 19:10:55
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Name
Muteb Alshammari
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Copyright holder
Affiliation
Rutgers University. School of Health Professions
AssociatedObject
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License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical
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windows xp
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2016-07-30T19:08:32
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2016-07-30T19:08:32
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