Psycho web: A machine learning platform for the diagnosis and classification of mental disorders

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

In this paper, we present the development of a platform to collect data from cases diagnosed with mental disorders. It includes the use of a Machine Learning classification algorithm, k-NN with TF-IDF, to automatically identify the type of mental disorder suffered by a patient given his/her symptoms, when evaluated by a mental health professional. The platform called “Psycho Web” has a friendly web interface that will allow ergonomic interaction between the mental health professional and the system. The dataset used for the initial evaluation of our platform is composed of 114 instances in total, 56% of which were obtained from the taxonomy proposed by ICD-10. The rest of the instances correspond to real cases, whose symptoms and diagnoses were taken by professionals who voluntarily collaborated with the project. A raw application of the algorithm to the data available shows results with errors that go down to 5%.

Original languageEnglish
Title of host publicationAdvances in Neuroergonomics and Cognitive Engineering - Proceedings of the AHFE 2019 International Conference on Neuroergonomics and Cognitive Engineering, and the AHFE International Conference on Industrial Cognitive Ergonomics and Engineering Psychology, 2019
EditorsHasan Ayaz
PublisherSpringer Verlag
Pages399-410
Number of pages12
ISBN (Print)9783030204723
DOIs
StatePublished - 1 Jan 2020
EventAHFE International Conference on Neuroergonomics and Cognitive Engineering, 2019 and the AHFE International Conference on Industrial Cognitive Ergonomics and Engineering Psychology, 2019 - Washington D.C., United States
Duration: 24 Jul 201928 Jul 2019

Publication series

NameAdvances in Intelligent Systems and Computing
Volume953
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

ConferenceAHFE International Conference on Neuroergonomics and Cognitive Engineering, 2019 and the AHFE International Conference on Industrial Cognitive Ergonomics and Engineering Psychology, 2019
Country/TerritoryUnited States
CityWashington D.C.
Period24/07/1928/07/19

Bibliographical note

Funding Information:
Acknowledgments. This work was supported by IDEIAGEOCA Research Group of Univer-sidad Politécnica Salesiana in Quito, Ecuador.

Publisher Copyright:
© Springer Nature Switzerland AG 2020.

Keywords

  • Diagnosis prediction
  • ICD-10
  • k-NN
  • Machine learning
  • Mental disorders
  • TF-IDF

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