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Machine Learning in Detecting Schizophrenia: An Overview

Gurparsad Singh Suri1, Gurleen Kaur1, Sara Moein2,*
1 College of Behavioral and Social Sciences, California Baptist University, Riverside, 92504, USA
2 Center for Innovation in Brain Science, University of Arizona, Tucson, 85721, USA
* Corresponding Author: Sara Moein. Email:

Intelligent Automation & Soft Computing 2021, 27(3), 723-735. https://doi.org/10.32604/iasc.2021.015049

Received 04 November 2020; Accepted 09 December 2020; Issue published 01 March 2021

Abstract

Schizophrenia (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.

Keywords

Support vector machine (SVM); deep neural network (DNN); magnetic resonance imaging (MRI); accuracy; classification; feature

Cite This Article

G. Singh 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.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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