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%.
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World Health Organization: International statistical classification of diseases and related health problems, Chapter V, 10th revision (1992)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2016)
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)
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)
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
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)
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)
Altman, N.S.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46(3), 175–185 (1992)
Trstenjak, B., Mikac, S., Donko, D.: KNN with TF-IDF based framework for text categorization. Procedia Eng 69, 1356–1364 (2014)
Green, P.: Marketing applications of MDS: assessment and outlook. J. Mark. 39, 24–31 (1975)
Carletta, J.: Assessing agreement on classification tasks: the kappa statistic. Comput. Linguist. 22(2), 249–254 (1996)
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This work was supported by IDEIAGEOCA Research Group of Universidad Politécnica Salesiana in Quito, Ecuador.
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Morillo, P., Ortega, H., Chauca, D., Proaño, J., Vallejo-Huanga, D., Cazares, M. (2020). Psycho Web: A Machine Learning Platform for the Diagnosis and Classification of Mental Disorders. In: Ayaz, H. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2019. Advances in Intelligent Systems and Computing, vol 953. Springer, Cham. https://doi.org/10.1007/978-3-030-20473-0_39
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DOI: https://doi.org/10.1007/978-3-030-20473-0_39
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