NDLI: Classification of Schizophrenia Data Using Support Vector Machine (SVM)

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Classification of Schizophrenia Data Using Support Vector Machine (SVM)

Content Provider	Scilit
Author	Rampisela, T. V. Rustam, Z.
Copyright Year	2018
Description	Journal: Journal of Physics: Conference Series Schizophrenia is a severe and chronic mental disorder. This disorder is marked with disturbances in thoughts, perceptions, and behaviours. Due to these disturbances that can trigger Schizophrenics to commit suicide or attempt to do so, Schizophrenics have a lower life expectancy than the general population. Schizophrenia is also difficult to diagnose as there is no physical test to diagnose it yet and its symptoms are very similar to several other mental disorders. Using Northwestern University Schizophrenia Data, this research aims to distinguish people who are Schizophrenics and people who are not. The data consists of 392 observations and 65 variables that are demographic data and clinician-filled Scale for the Assessment of Positive and Negative Symptoms questionnaires. Classification method used is machine learning with Support Vector Machines (SVM). Simulations are done with different data and percentage of training data. In each simulation, accuracy is measured. Model performance validation and evaluation are done by averaging ten times Hold-Out Validations that were done. In conclusion, SVM successfully classified Schizophrenia data with final accuracy of 90.1%. Furthermore, SVM with linear kernel and Gaussian kernel reached an accuracy of 95.0% in at least one simulation in classifying Schizophrenia data.
Related Links	http://iopscience.iop.org/article/10.1088/1742-6596/1108/1/012044/pdf
ISSN	17426588
e-ISSN	17426596
DOI	10.1088/1742-6596/1108/1/012044
Journal	Journal of Physics: Conference Series
Issue Number	1
Volume Number	1108
Language	English
Publisher	IOP Publishing
Publisher Date	2018-11-01
Access Restriction	Open
Subject Keyword	Journal: Journal of Physics: Conference Series Medical Informatics Schizophrenia Data
Content Type	Text
Resource Type	Article
Subject	Physics and Astronomy

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