AffiliationCenter for Innovation in Brain Science, University of Arizona
Deep neural network (DNN)
Magnetic resonance imaging (MRI)
Support vector machine (SVM)
MetadataShow full item record
PublisherTech Science Press
CitationG. S. Suri, G. Kaur and S. Moein, "Machine learning in detecting schizophrenia: an overview," Intelligent Automation & Soft Computing, vol. 27, no.3, pp. 723–735, 2021.
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AbstractSchizophrenia (SZ) is a mental heterogeneous psychiatric disorder with unknown cause. Neuroscientists postulate that it is related to brain networks. Recently, scientists applied machine learning (ML) and artificial intelligence for the detection, monitoring, and prognosis of a range of diseases, including SZ, because these techniques show a high performance in discovering an association between disease symptoms and disease. Regions of the brain have significant connections to the symptoms of SZ. ML has the power to detect these associations. ML interests researchers because of its ability to reduce the number of input features when the data are high dimensional. In this paper, an overview of ML models for detecting SZ disorder is provided. Studies are presented that applied magnetic resonance imaging data and physiological signals as input data. ML is utilized to extract significant features for predicting and monitoring SZ. Reviewing a large number of studies shows that a support vector machine, deep neural network, and random forest predict SZ with a high accuracy of 70%–90%. Finally, the collected results show that ML methods provide reliable answers for clinicians when making decisions about SZ patients. © 2021 Mark A. Lemley & Bryan Casey.
NoteOpen access journal
VersionFinal published version
Except where otherwise noted, this item's license is described as This work is licensed under a Creative Commons Attribution 4.0 International License. Copyright is held by the author(s) or the publisher. If your intended use exceeds the permitted uses specified by the license, contact the publisher for more information.