Diagnosing Schizophrenia: A Deep Learning Approach

Keywords

Deep learning; Fmri; Logistic regression; Random forest; Schizophrenia; SVM

Abstract

This paper presents a new method for diagnosing schizophrenia using deep learning. This experiment used a secondary dataset supplied by the National Institute of Health. The experiment analyzes the dataset and identifies schizophrenia using traditional machine learning methods such as logistic regression, support vector machines, and random forest. Finally, a deep neural network with three hidden layers is applied to the dataset. The results show that the neural network model yielded the highest accuracy, suggesting that deep learning may be a feasible method for diagnosing schizophrenia.

Publication Date

8-15-2018

Publication Title

ACM-BCB 2018 - Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

Number of Pages

549-

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/3233547.3233658

Socpus ID

85056094408 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85056094408

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