The week's pick
Random Articles
Reseach Article
Classification of Mental Disorders Figures based on Soft Computing Methods
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 117 - Number 2 |
| Year of Publication: 2015 |
| Authors: Jabar H. Yousif |
10.5120/20524-2857
|
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
Abstract
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.
References
- Abusaa, M. , Diederich, J. and Al Ajmi, A. (2004). Web mining and mental health. In: IAWTIC 2004 Proceedings. International Conference on Intelligent Agents, Web Technologies and Internet Commerce, Gold Coast, Queensland. 12-14 July 2004.
- Adelman, H. & Taylor, L. (2006). The current status of mental health in schools: A policy and practice analysis. Los Angeles, CA: Center for Mental Health in Schools at UCLA. http://smhp. psych. ucla. edu/currentstatusmh. htm
- Akyol, D. E. (2004). Applications of neural networks to heuristic scheduling algorithms. Computers and Industrial Engineering, 46, 679-696.
- Armstrong, R. , et. al,(2008). , Systematic reviews in public health, International Encyclopedia of Public Health, 297-301.
- Bauer, S. , Percevic, R. , Okon, E. , Meerman, R. , & Kordy, H. (2003). Use of text messaging in the aftercare of patients with bulimia nervosa. European Eating Disorders Review, 11, 279–290.
- Beyer, H. -G. , &Schwefel, H. -P. (2002). Evolution strategies: A comprehensive introduction. Natural Computing: An International Journal, 1(1), 3–52. DOI: 10. 1023/A:1015059928466
- Bonissone, P (2002). Hybrid Soft Computing for Classification and Prediction Applications. Conferencia Invitada. 1st International Conference on Computing in an Imperfect World (Soft-Ware 2002), Belfast. DOI: 10. 1007/3-540-46019-5_28.
- Chang, W. C. , et al. (2010), Factors affecting the use of health examinations by the elderly in Taiwan, Archives of Gerontology and Geriatrics, 50(1), S11-S16.
- Clarke, G. , Lynch, F. , Spofford, M. , & DeBar , L. (2006). Trends influencing future delivery of mental health services in large healthcare systems. Clinical Psychology: Science and Practice, 13 (3) , 287-292. doi:10. 1111/j. 1468-2850. 2006. 00040.
- C. R. S. Lopes, et. al. (2002) "Neural Networks for the analysis of Common Mental Disorders Factors. " Proc. of the VII Brazilian Symposium on Neural Networks (SBRN'02) IEEE Computer Society Washington, DC, USA ©2002 , ISBN:0-7695-1709-9
- Gal Kazas & Michael Margaliot , "Visualizing the topology of mental disorders using selforganizing feature maps". http://www. eng. tau. ac. il/~michaelm/kazas. pdf
- Hadzic, M. , Chen, M. , & Dillon ,T. (2008) . Towards the mental health ontology , proceeding of the IEEE International Conf. on Bioinformatics and Biomedicine, USA. DOI: 10. 1109/BIBM. 2008. 59
- Imianvan A. A. and Obi J. C. (2011): Diagnostic evaluation of Hepatitis utilizing Fuzzy Clustering Means, World Journal of Applied Science and Technology, Vol. 3. No. 1 23-3.
- Jabar H. yousif, Mabruk a. Fekihal , (2012), Neural Approach For Determining Mental Health Problems , Journal 0f Computing, volume 4, issue 1, , ISSN 2151-9617.
- Jabar H. Yousif, Information Technology Development, LAP LAMBERT Academic Publishing, Germany ISBN 9783844316704, 2011.
- Joachim Diederich , et. al, (2007). Ex-ray: Data mining and mental health, Applied Soft Computing 7 (2007) 923–928. doi:10. 1016/j. asoc. 2006. 04. 007.
- Joachim Diederich , P. Yellowlees, A. Al-Ajmi(2003), Ex-Ray: Text Classification and the Assessment of Mental Health, Bruza, P. , Thom, J. (Eds. ), Proceedings of the Eighth Australian Document Computing Symposium, Canberra, Australia (15 December 2003) 75–76. Canberra: CSIRO Discovery Centre http://joachimdiederich. com/assets/Ex-RayADCS03. pdf
- Lippman R. (1987), An introduction to computing with neural nets. IEEE Trans. ASSP Magazine 4, 4-22.
- Lodhi H. , et. al,(2002). Text classification using string kernels. Journal of Machine Learning Research, 2, 419–444. Paris: EC2 & Cie. doi=10. 1. 1. 130. 2853
- Mabruk Ali Fekihal and Jabar H Yousif,(2012), "Self-Organizing Map Approach for Identifying Mental Disorders", International Journal of Computer Applications 45(7):25-30, Published by Foundation of Computer Science, New York, USA
- Medical dictionary. http://www. medilexicon. com/medicaldictionary. php
- Teuvo Kohonen, ,(2001), Self-Organizing Maps, (3rd edition) Springer , ISBN 3540679219 .
- Zadeh, L. A. (2001). Applied Soft Computing. Applied Soft Computing 1, 1–2.
Index Terms
Keywords