IEEE Access (Jan 2019)

Automatic Detection and Diagnosis of Neurologic Diseases

  • Luciano Comin Nunes,
  • Placido Rogerio Pinheiro,
  • Mirian Caliope Dantas Pinheiro,
  • Marum Simao Filho,
  • Rafael Espindola Comin Nunes,
  • Pedro Gabriel Caliope Dantas Pinheiro

DOI
https://doi.org/10.1109/ACCESS.2019.2899216
Journal volume & issue
Vol. 7
pp. 29924 – 29941

Abstract

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This paper presents a hybrid proposal of a specialist system with a multicriteria decision analysis method, aiming to subsidize decisions in the early diagnosis of psychological disorders. Such disorders can even cause the inability of professionals from various fields of activity, notably of those related to innovation and automation of critical business processes, which exert more pressure on results. These cited disorders cause harm to the professional, to his family, to the company for which he works, to the productive system, and to the social security of a nation. The lack of early diagnosis and the undue attention given to the symptoms only provide reactive and late measures, when the losses have already occurred, in addition to the fact that the professional shows signs of incapacity for work and social life. This paper presents a model that facilitates the process of early diagnosis of various psychological disorders from the qualitative and comparative analysis of events and criteria, using multicriteria methodology associated with a specialist system. Therefore, the proposed model constitutes a modern and consistent tool that contributes to the decision to indicate diagnoses in psychological disorders. Among the various psychological disorders described and categorized in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders of the American Psychiatric Association, this paper highlights the following: schizophrenia spectrum disorders, bipolar disorder, depressive disorders, anxiety disorders, obsessive-compulsive disorder, trauma-related disorders and stressors, dissociative disorders, and disorders related to substances and adverse disorders.

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