Predictive Strength of Bayesian Networks for Diagnosis of Depressive Disorders

  • Blessing OjemeEmail author
  • Audrey Mbogho
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 56)


Increasing cases of misdiagnosis of mental disorders in Nigeria despite the use of the international standards provided in the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) and International Classification of Diseases (ICD-10) calls for an approach that takes cognizance of the socio-economic difficulties on the ground. While a growing recognition of the potential of artificial intelligence (AI) techniques in modeling clinical procedures has led to the design of various systems to assist clinicians in decision-making tasks in physical diseases, little attention has been paid to exploring the same techniques in the mental health domain. This paper reports the preliminary findings of a study to investigate the predictive strength of Bayesian networks for depressive disorders diagnosis. An automatic Bayesian model was constructed and tested with a real-hospital dataset of 580 depression patients of different categories and 23 attributes. The model predicted depression and its severity with high efficiency.


Artificial intelligence Bayesian networks Mental health Depression disorders Psychiatric diagnosis 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of Cape TownRondeboschSouth Africa
  2. 2.Department of Mathematics and PhysicsPwani UniversityKilifiKenya

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