Generalized Anxiety Disorder : Prediction using ANN
Keywords:
Artificial Neural Network (ANN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Generalized Anxiety Disorder (GAD)Abstract
Artificial Neural Network plays an important role in medical diagnostics field and used by medical practitioners and domain specialists for diagnosis and treatment with ultimate accuracy. In this paper, a medical diagnosis system is proposed for predicting Generalized Anxiety Disorder (GAD). In today’s world of computational Intelligence, Swarm Intelligence technique is one of the successive ways to solve hard medical problems. Particle Swarm Optimization (PSO) imitates the behavior of a swarm of insects or a group of fish or birds. In this paper the relative advantages of genetic algorithm, Particle Swarm Optimization and Artificial Neural Network (ANN) are combined to achieve the desired accuracy. ANN’s are often used as a powerful discriminating classifier for tasks in medical diagnosis for early detection of diseases. The data set on this study is composed of 200 patients with various sign symptoms. The objective of this paper is to determine the weights of the neural network using genetic algorithm in less number of iterations and PSO algorithm for feature Reduction. For training the network Quasi-newtonalgorithm is used in this study using various training algorithm parameters. The accuracy obtained using this approach is 98.56%.
References
Anxiety Dsorders Association of America Available: http://www.adaa.org
”Generalized Anxiety Disorder: When Worry Gets Out of Control”-national institute of mental Health U.S. Department of Health and Human Services national institutes of Health
Singh Y, and Chauhan, A.S. “Neural Networks in Data mining”, Journal of Theoretical and Applied Information Technology, Vol. 5, No. 1, Pp. 37-42,2009
Roy ChowdhuryDilip, BhattacharjeeDipanwita, "Swarm Intelligence and ANN Techniques for predicting Neonatal Disease" NCRTCSA-2014
Gupta preeti,KaurBikrampal, “Accuracy Enhancement of Heart Disease Diagnosis System Using Neural Network and Genetic Algorithm”International Journal of Advanced Research in Computer Science and Software Engineering 4(8), August - 2014, pp. 160-166
Sivagowry.S ,Dr.Durairaj.M “PSO - An Intellectual Technique for Feature Reduction on Heart Malady Anticipation Data”Volume 4, Issue 9, September 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering
Valavanis, I.K.,School Of Electrical And Computer Engineering, National Technical University Of Athens, 9 heroonPolytechneiou Str,15780 Zographou, Geece, Mougiakakou, S.G; Grimaldi, Ka Nikita Ks,”. Analysis Of Postprandial Lipemia As A Cardiovascular Disease Risk factor Using Genetic And Clinical Information: An Artificial Neural network Perspective”, Engineering In Medicine And Biology Society 2008.Embs 2008.30th Annual International IEEE Embs Conference Vancouver, British Columbia, Canada, August 20-24, 2008 Pages 4609-4612.
Goldberg E., “Genetic algorithms in search, optimization, and machine learning” , London, Addison-Wesley, 1989.
KaregowdaGowda, Manjunath A.S. and JayaramM.A.,”Application of Genetic Agorithm optimized Neural Network connection weights for Medical Diagnosis of PIMA Indians Diabets”,International Journal on Soft Computing(IJSC), Vol.2,No.2,Pp. 15-23,2011
Parimala M., Sumalatha G. and N. Zareena, “A heuristic Approach to the CHILLI Expert System using PSO Algorithm”, Int. J of Advanced research in Computer Science and Software Engineering(IJARCSSE),Vol.3,Issue 7, Pp. 1126-1130, ISSN: 2277 128X, July 2013
Alperunler, Alper Murat and RatnaBabuChinnam, “m2PSO: A maximum relevance minimum redundancy feature selection method based on Swarm Intellignce for SVM Classification”, Elsevier, 2011, pp 4625-4641.
Shi Y. and Eberhart R., “Parameter Selection in Particle Swarm Optimization”, proceedings of the Seventh Annual Conference on Evolutionary Programming,Pp.591-601,1998.
D. R. Chowdhury, R.K Samanta and M. Chatterjee. "Artificial Neural Network Model for Neonatal Disease Diagnosis", International Journal of Artificial Intelligence and Expert Systems (IJAE), Volume (2) : Issue (3), 2011
International Classification of Diseases(ICD)”. World Health Organization. Retrieved 23 November 2010.
“Neuro Intelligence using Alyuda”, http://www.alyuda.com
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
