IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i11p6099-d569481.html
   My bibliography  Save this article

Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification

Author

Listed:
  • Joel Weijia Lai

    (Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore)

  • Candice Ke En Ang

    (Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore
    MOH Holdings Pte Ltd, 1 Maritime Square, Singapore 099253, Singapore)

  • U. Rajendra Acharya

    (Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi 599489, Singapore
    Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Clementi 599491, Singapore
    Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan)

  • Kang Hao Cheong

    (Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore)

Abstract

Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software with allowable thresholds to make deterministic approximate conclusions. In comparison to traditional technologies in healthcare, artificial intelligence enhances the process of data analysis without the need for human input, producing nearly equally reliable, well defined output. Schizophrenia is a chronic mental health condition that affects millions worldwide, with impairment in thinking and behaviour that may be significantly disabling to daily living. Multiple artificial intelligence and machine learning algorithms have been utilized to analyze the different components of schizophrenia, such as in prediction of disease, and assessment of current prevention methods. These are carried out in hope of assisting with diagnosis and provision of viable options for individuals affected. In this paper, we review the progress of the use of artificial intelligence in schizophrenia.

Suggested Citation

  • Joel Weijia Lai & Candice Ke En Ang & U. Rajendra Acharya & Kang Hao Cheong, 2021. "Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification," IJERPH, MDPI, vol. 18(11), pages 1-20, June.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:11:p:6099-:d:569481
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/11/6099/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/11/6099/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ravi Bansal & Lawrence H Staib & Andrew F Laine & Xuejun Hao & Dongrong Xu & Jun Liu & Myrna Weissman & Bradley S Peterson, 2012. "Anatomical Brain Images Alone Can Accurately Diagnose Chronic Neuropsychiatric Illnesses," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-21, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Arno Klein & Satrajit S Ghosh & Forrest S Bao & Joachim Giard & Yrjö Häme & Eliezer Stavsky & Noah Lee & Brian Rossa & Martin Reuter & Elias Chaibub Neto & Anisha Keshavan, 2017. "Mindboggling morphometry of human brains," PLOS Computational Biology, Public Library of Science, vol. 13(2), pages 1-40, February.
    2. Raymond Salvador & Joaquim Radua & Erick J Canales-Rodríguez & Aleix Solanes & Salvador Sarró & José M Goikolea & Alicia Valiente & Gemma C Monté & María del Carmen Natividad & Amalia Guerrero-Pedraza, 2017. "Evaluation of machine learning algorithms and structural features for optimal MRI-based diagnostic prediction in psychosis," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-24, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:11:p:6099-:d:569481. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.