African Journal of
Mathematics and Computer Science Research

  • Abbreviation: Afr. J. Math. Comput. Sci. Res.
  • Language: English
  • ISSN: 2006-9731
  • DOI: 10.5897/AJMCSR
  • Start Year: 2008
  • Published Articles: 231

Full Length Research Paper

A fuzzy inference system for predicting depression risk levels

EKONG, Victor E
  • EKONG, Victor E
  • Department of Computer Science, Faculty of Science, University of Uyo, Uyo, Akwa Ibom State, Nigeria
  • Google Scholar
EKONG, Uyinomen O
  • EKONG, Uyinomen O
  • Department of Computer Science, Faculty of Science, University of Uyo, Uyo, Akwa Ibom State, Nigeria
  • Google Scholar
UWADIAE, Enobakhare E
  • UWADIAE, Enobakhare E
  • Department of Mental Health, University of Benin Teaching Hospital, Benin City, Nigeria
  • Google Scholar
ABASIUBONG, Festus
  • ABASIUBONG, Festus
  • Department of Mental Health, University of Uyo Teaching Hospital, Uyo, Akwa Ibom State, Nigeria
  • Google Scholar
ONIBERE, Emmanuel A.
  • ONIBERE, Emmanuel A.
  • Department of Computer Science, Faculty of Physical Sciences University of Benin, Benin City, Nigeria
  • Google Scholar


  •  Accepted: 13 November 2013
  •  Published: 28 November 2013

Abstract

This paper reports the findings from the experimental study of an intelligent system driven by Fuzzy Logic (FL) for depression risk diagnosis. Depression is a common psychological disorder that can cause serious health challenges if it remains undiagnosed, misdiagnosed or untreated. It represents a major public health problem identified by the world health organization (WHO) to have affected a vast majority of the productive adult population. The confusing nature of the disease symptoms makes it difficult for physicians using psychometric assessment tools alone to determine the severity of the disease. With advances in artificial intelligence (AI), intelligent computing has accelerated new approaches that can enhance medical decision support services. This paper describes research results in the development of a fuzzy driven system to determine the depression risk levels of patients. The system is implemented and simulated using MATLAB fuzzy tool box. The result of the system is consistent with an expert specialist’s opinion on evaluating the performance of the system.

Key words: Depression, depression risk, fuzzy logic, severity level, matlab, membership functions.