Schizophrenia is a major psychiatric disorder which significantly reduces the quality of life. Early treatment is extremely important in order to mitigate the long-term negative effects. Because of that, reliable diagnosis of schizophrenia is of big interest. In this paper, machine learning based diagnostics of schizophrenia is designed. Classification models are applied to event-related potentials (ERPs) calculated from electroencephalo-gram (EEG) records of patients and healthy subjects performing modification of the visual cued Go-NoGo task. The sample consisted of 200 adult individuals, with an age ranging between 18 and 50 years. In order to apply machine learning models various features are extracted from ERPs. Process of feature extraction is parametrized through a special procedure and parameters of this procedure are selected through a grid-search technique along with model hyperparameters. Feature extraction is followed by Sequential Feature Selection transformation in order to prevent overtitting and reduce computational complexity. Support vector machines and Random Forest models are trained on the resulting feature set. Sensitivity and specificity of the best model are 91% and 91.7% respectively.

Original languageEnglish
Title of host publicationProceedings - 6th Scientific School "Dynamics of Complex Networks and their Applications", DCNA 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages252-255
Number of pages4
ISBN (Electronic)9781665461740
ISBN (Print)9781665461740
DOIs
StatePublished - 21 Oct 2022
Event6th Scientific School "Dynamics of Complex Networks and their Applications", DCNA 2022 - Kaliningrad, Russian Federation
Duration: 14 Sep 202216 Sep 2022

Publication series

NameProceedings - 6th Scientific School "Dynamics of Complex Networks and their Applications", DCNA 2022

Conference

Conference6th Scientific School "Dynamics of Complex Networks and their Applications", DCNA 2022
Country/TerritoryRussian Federation
CityKaliningrad
Period14/09/2216/09/22

    Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Control and Optimization

    Research areas

  • Electroencephalogram, event-related potential, random forest, schizophrenia, support vector machine

ID: 100730706