Diagnosis of Schizophrenia from EEG signals Using ML Algorithms

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Early treatment is required to control the symptoms and serious complications caused by schizophrenia (SZ). People suffering from SZ require lifelong treatment. The use of machine learning (ML) models to detect various health problems such as SZ has received considerable attention from researchers in recent years. This study investigated the effectiveness of various ML models to detect and predict SZ using electroencephalogram data. A dataset of 14 healthy schizophrenic patients was used, and 12 features were extracted after applying independent component analysis. Three traditional ML models (logistic regression, support vector machine, and K-nearest neighbors) and a convolutional neural network (CNN) were trained, and their performance was compared. Results demonstrated that the CNN model outperformed the other three models with the highest accuracy score of 95% on validation data. Our results highlight the potential of using ML in the early detection and prediction of SZ, which can help in timely and effective treatment.

Original languageEnglish
Title of host publicationProceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
EditorsXingpeng Jiang, Haiying Wang, Reda Alhajj, Xiaohua Hu, Felix Engel, Mufti Mahmud, Nadia Pisanti, Xuefeng Cui, Hong Song
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2564-2570
Number of pages7
ISBN (Electronic)9798350337488
DOIs
Publication statusPublished - 2023
Event2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 - Istanbul, Turkey
Duration: Dec 5 2023Dec 8 2023

Publication series

NameProceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

Conference

Conference2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
Country/TerritoryTurkey
CityIstanbul
Period12/5/2312/8/23

Keywords

  • CNN
  • EEG
  • KNN
  • SVM
  • Schizophrenia
  • logistic Regression

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Automotive Engineering
  • Modelling and Simulation
  • Health Informatics

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