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Psycho Web: A Machine Learning Platform for the Diagnosis and Classification of Mental Disorders

  • Paulina MorilloEmail author
  • Holger Ortega
  • Diana Chauca
  • Julio Proaño
  • Diego Vallejo-Huanga
  • María Cazares
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 953)

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%.

Keywords

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

Notes

Acknowledgments

This work was supported by IDEIAGEOCA Research Group of Universidad Politécnica Salesiana in Quito, Ecuador.

References

  1. 1.
    World Health Organization: International statistical classification of diseases and related health problems, Chapter V, 10th revision (1992)Google Scholar
  2. 2.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2016)zbMATHGoogle Scholar
  3. 3.
    David, A., Blamire, A., Breiter, H.: Functional magnetic resonance imaging: a new technique with implications for psychology and psychiatry. Br. J. Psychiatry 164(1), 2–7 (1994)CrossRefGoogle Scholar
  4. 4.
    Lehmann, C., Koenig, T., Jelic, V., Prichep, L., John, R.E., Wahlund, L.O., Dierks, T.: Application and comparison of classification algorithms for recognition of Alzheimer’s disease in electrical brain activity (EEG). J. Neurosci. Methods 161(2), 342–350 (2007)CrossRefGoogle Scholar
  5. 5.
    Cao, B., Cho, R.Y., Chen, D., Xiu, M., Wang, L., Soares, J.C., Zhang, X.Y.: Treatment response prediction and individualized identification of first-episode drug-naïve schizophrenia using brain functional connectivity. Mol. Psychiatry, 1–8 (2018).  https://doi.org/10.1038/s41380-018-0106-5
  6. 6.
    Gutiérrez Miras, M.G., Peñas Martínez, L., Santiuste de Pablos, M., García Ruipérez, D., Ochotorena Ramírez, M.M., San Eustaquio Tudanca, F., Cánovas Martínez, M.: Comparación de los sistemas de clasificación de los trastornos mentales CIE 10 y DSM IV. Atlas VPM 5, 220–222 (2008)Google Scholar
  7. 7.
    Koch, N., Knapp, A., Zhang, G., Baumeister, H.: UML-based web engineering. In: Web Engineering: Modelling and Implementing Web Applications, pp. 157–191. Springer, London (2008)Google Scholar
  8. 8.
    Altman, N.S.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46(3), 175–185 (1992)MathSciNetGoogle Scholar
  9. 9.
    Trstenjak, B., Mikac, S., Donko, D.: KNN with TF-IDF based framework for text categorization. Procedia Eng 69, 1356–1364 (2014)CrossRefGoogle Scholar
  10. 10.
    Green, P.: Marketing applications of MDS: assessment and outlook. J. Mark. 39, 24–31 (1975)CrossRefGoogle Scholar
  11. 11.
    Carletta, J.: Assessing agreement on classification tasks: the kappa statistic. Comput. Linguist. 22(2), 249–254 (1996)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Paulina Morillo
    • 1
    Email author
  • Holger Ortega
    • 1
  • Diana Chauca
    • 2
  • Julio Proaño
    • 1
  • Diego Vallejo-Huanga
    • 1
    • 3
    • 4
  • María Cazares
    • 5
  1. 1.IDEIAGEOCA Research GroupUniversidad Politécnica SalesianaQuitoEcuador
  2. 2.Department of Computer ScienceUniversidad Politécnica SalesianaQuitoEcuador
  3. 3.Department of MathematicsUniversidad San Francisco de QuitoQuitoEcuador
  4. 4.Department of Physics and MathematicsUniversidad de las AméricasQuitoEcuador
  5. 5.Department of PsychologyUniversidad Politécnica SalesianaQuitoEcuador

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