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Classification of Mental Disorders Figures based on Soft Computing Methods

by Jabar H. Yousif
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 117 - Number 2
Year of Publication: 2015
Authors: Jabar H. Yousif

Jabar H. Yousif . Classification of Mental Disorders Figures based on Soft Computing Methods. International Journal of Computer Applications. 117, 2 ( May 2015), 5-11. DOI=10.5120/20524-2857

@article{ 10.5120/20524-2857,
author = { Jabar H. Yousif },
title = { Classification of Mental Disorders Figures based on Soft Computing Methods },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 117 },
number = { 2 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 5-11 },
numpages = {9},
url = { },
doi = { 10.5120/20524-2857 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:58:13.830463+05:30
%A Jabar H. Yousif
%T Classification of Mental Disorders Figures based on Soft Computing Methods
%J International Journal of Computer Applications
%@ 0975-8887
%V 117
%N 2
%P 5-11
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

The purpose of this paper is to present the design and implementation of Soft Computing approaches (SOFM and MLP) for classifying mental disorders figures. World Health Organization (WHO) referred to the higher rates of mental disorders as a result of the pressures of life and its difficulties. This prompted the non-governmental organizations and civil institutions to increase research and reduce the risk of mental illness. However, despite all these efforts, the researchers still did not achieved the anticipated results. The classification of mental disorders is a significant concern for researchers as well as health care service providers. The comparison study is tested two different techniques under the same environment , conditions and using same data. The MLP achieved high accuracy of (99%), while SOFM achieved an accuracy of (97%) in the classification phase for predicting the desired output. In the meaning of data amount usage, both MLP & SOMF are used a small amount of data to perform the training phase. While other approaches like SVM are huge data sets to perform the training phase. Consequently, using a few data in the learning network phase leads to a rapid process and getting a results in short time.

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Index Terms

Computer Science
Information Sciences


Mental illness MLP SOFM text clustering text classification unsupervised earning.