Identification of Schizophrenia from other Major Psychiatric Disorders using Machine Learning Techniques

DOI

10.2991/978-94-6239-628-9_33How to use a DOI?

Keywords

Schizophrenia; Machine Learning (ML); Electroencephalogram (EEG); biomarker

Abstract

Schizophrenia is a chronic mental illness, which is characterized by impairment of thoughts, percepts, affect, and behaviors, as a result of which there is significant functional impairment. The standard diagnosis for this condition is greatly dependent upon clinical evaluation, which is inherently time-consuming, subjective, as well as poor in distinguishing between schizophrenia and other chronic mental illnesses. Though EEG analysis has been recognized as a potential marker, previous studies have been criticized for employing small samples, moderate level of correctness in classification, as well as poor coverage of disorders.

This paper proposes to examine whether an EEG-based solution using machine learning exists for this issue. A publicly available rest state EEG database of 945 individuals, including 850 patients diagnosed with six types of mental illness, as well as 95 controls, is used in this study. Six types of machine learning classifiers, namely Decision Tree, Random Forest, Support Vector Machine, Gradient Boosting, AdaBoost, and XGBoost, have been used to examine which ones work in terms of correct classification of results. The results of gradient boosters in classifying schizophrenia from normal controls is 82.98%, schizophrenia from mood disorders of 85.98%, schizophrenia from obsessive-compulsive disorder of 89.36%, but only 53.19% for schizophrenia from trauma and stress-related disorders, respectively, showed that AdaBoost performed either equal to, or better than, gradient boosters in several instances, including schizophrenia vs. obsessive-compulsive disorder at 91.49%.

The results support EEG as an actual potential marker for schizophrenia diagnosis, but they also support nomological validity issues in current methods, which require better features, better algorithms, and more data to make them useful for diagnosis.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Aman Kapoor
AU  - Vishal Meena
AU  - Sandhir Kumar
AU  - Madhu Kumari
AU  - Shalini Mahato
PY  - 2026
DA  - 2026/03/31
TI  - Identification of Schizophrenia from other Major Psychiatric Disorders using Machine Learning Techniques
BT  - Proceedings of the International Conference on Recent Trends in Intelligent Computing, Manufacturing, and Electronics (rTIME 2025)
PB  - Atlantis Press
SP  - 369
EP  - 376
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-628-9_33
DO  - 10.2991/978-94-6239-628-9_33
ID  - Kapoor2026
ER  -