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A deep learning approach to diagnosing schizophrenia

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Date Issued:
2019
Abstract/Description:
In this article, the investigators present a new method using a deep learning approach to diagnose schizophrenia. In the experiment presented, the investigators have used a secondary dataset provided by National Institutes of Health. The aforementioned experimentation involves analyzing this dataset for existence of schizophrenia using traditional machine learning approaches such as logistic regression, support vector machine, and random forest. This is followed by application of deep learning techniques using three hidden layers in the model. The results obtained indicate that deep learning provides state-of-the-art accuracy in diagnosing schizophrenia. Based on these observations, there is a possibility that deep learning may provide a paradigm shift in diagnosing schizophrenia.
Title: A deep learning approach to diagnosing schizophrenia.
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Name(s): Barry, Justin, Author
Valliyil Thankachan, Sharma, Committee Chair
Gurupur, Varadraj, Committee CoChair
Jha, Sumit Kumar, Committee Member
Ewetz, Rickard, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2019
Publisher: University of Central Florida
Language(s): English
Abstract/Description: In this article, the investigators present a new method using a deep learning approach to diagnose schizophrenia. In the experiment presented, the investigators have used a secondary dataset provided by National Institutes of Health. The aforementioned experimentation involves analyzing this dataset for existence of schizophrenia using traditional machine learning approaches such as logistic regression, support vector machine, and random forest. This is followed by application of deep learning techniques using three hidden layers in the model. The results obtained indicate that deep learning provides state-of-the-art accuracy in diagnosing schizophrenia. Based on these observations, there is a possibility that deep learning may provide a paradigm shift in diagnosing schizophrenia.
Identifier: CFE0007429 (IID), ucf:52737 (fedora)
Note(s): 2019-05-01
M.S.
Engineering and Computer Science, Computer Science
Masters
This record was generated from author submitted information.
Subject(s): Schizophrenia -- fMRI -- Deep Learning -- Random Forest -- SVM -- Logistic Regression.
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0007429
Restrictions on Access: public 2019-05-15
Host Institution: UCF

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