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Title: | Analytical Hierarchy Process Model for Severity of Risk Factors Associated with Type 2 Diabetes |
Authors: | Baha, B.Y Wajiga, G.M Blamah, N.V Adewumi, A.O |
Keywords: | artificial neural network. |
Issue Date: | 18-Oct-2013 |
Publisher: | AcademicJournals |
Series/Report no.: | Vol.8;No.39;Pp 1906-1910 |
Abstract: | Type 2 diabetes has been an increasing public health problem with an estimated forecast of 300 million
around the world by the year 2025. It places a serious constraint on individual’s activities caused by
hyperglycemia resulting from defects in insulin secretion, insulin action or both. Although extensive
epidemiological researches have shown an association between various risk factors and the
development of type 2 diabetes, there has been no research on the measurement or determination of
the relative severity of these risk factors regarding their contributions to the incidence and prevalence
of type 2 diabetes. In this research, 13 risk factors associated with type 2 diabetes were identified from
epidemiological studies. The degree of severity of these risk factors was ascertained by professionals
using structured Liket format with 6 choices. The data obtained were used in ranking the risk factors,
which assisted in selecting the most contributing risk factors to the development of type 2 diabetes.
The result revealed that heredity contributes as high as 0.5388; obesity contributes 0.1038; physical
inactivity contributes 0.0230; dietary contributes 0.0230; age contributes 0.1038; IGT contributes 0.1038;
and gestational diabetes is 0.1038. This result could serve as input to neural network model. |
URI: | http://hdl.handle.net/123456789/557 |
ISSN: | 1992-2248 |
Appears in Collections: | Computer Science
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