Scientific Research and Essays

  • Abbreviation: Sci. Res. Essays
  • Language: English
  • ISSN: 1992-2248
  • DOI: 10.5897/SRE
  • Start Year: 2006
  • Published Articles: 2740

Full Length Research Paper

Analytical hierarchy process model for severity of risk factors associated with type 2 diabetes

B. Y. Baha1*, G. M. Wajiga2, N. V. Blamah3 and A. O. Adewumi3
  1Information Technology and Systems, Northeast Region, Mainstreet Bank Limited, Nigeria. 2Department of Computer Science, Federal University of Technology, Yola, Nigeria. 3Centre for Artificial Intelligence Research, School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa.
Email: [email protected]

  •  Accepted: 31 May 2013
  •  Published: 18 October 2013



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.


Key words: Type 2 diabetes, severity, risk factors, analytical hierarchy process, artificial neural network.