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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/557

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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