Article Sidebar
Published:
May 13, 2022DOI:
10.4314/njt.v40i5.21Keywords:
Article Details
Copyright belongs to the Faculty of Engineering, University of Nigeria, Nsukka, Nigeria
The contents of the articles are the sole opinion of the author(s) and not of NIJOTECH.
NIJOTECH allows open access for distribution of the published articles in any media so long as whole (not part) of articles are distributed.
A copyright and statement of originality documents will need to be filled out clearly and signed prior to publication of an accepted article. The Copyright form can be downloaded from https://www.nijotech.com/downloads/COPYRIGHT%20FORM.pdf while the Statement of Originality is in Statement of Originality.pdf (nijotech.com)
For articles that were developed from funded research, a clear acknowledgement of such support should be mentioned in the article with relevant references. Authors are expected to provide complete information on the sponsorship and intellectual property rights of the article together with all exceptions.
It is forbidden to publish the same research report in more than one journal.
Main Article Content
Application of neural network algorithm for schizophrenia diagnosis
Abstract
Schizophrenia is a prolonged mental condition that affects functional impairment in work, interpersonal relationships, and self-care. This research was aimed at developing a neural network model to diagnose schizophrenia using text data acquired from patients’ records. The model was developed from datasets obtained from Neuropsychiatric Hospital in Yaba and the Lagos University Teaching Hospital, both in Lagos, Nigeria, using Python programming language and is provided with significant features from data sets to learn patterns within the training data and perform classification on the test data. The results show that the model produced a test accuracy of 85%, specificity of 95% and a precision of 93%. These results indicate that the model can be used for effective computer-aided diagnosis of schizophrenia.
