Intelligent system for diagnosis of asthma severity using ANN

Authors

  • Ashish Patel NIT Raipur
  • M K Verma
  • Qamar Rahman

DOI:

https://doi.org/10.30732/ijbbb.20160101003

Keywords:

ANN, Intelligent system, Asthma, DSS

Abstract

Asthma is major public health problem due to its incurable nature and misdiagnosis. The severity of asthma leading to the stage of death is because of late detection and diagnosis of Asthma. In the field of treatment of Asthma the groups of expert doctors are limited. The motivation behind taking up this research work is the thought that if these experts’ knowledge and experience can be made available to a wide section of medical practitioners and up to some extents to susceptible patients then this disease can be diagnosed at its early stage and hence its treatment at proper time may alleviate the grievance of this disease. The present study is focused on development of a computer based Inference System with capability to diagnose the degree of severity of asthma based on the knowledge and experience of experts in this field. Developed Inference System is based on the philosophy of Artificial Neural Network. The performance of Inference System developed for the diagnosis of Asthma is quite encouraging and acceptable. Afterwards analysis of this inference system has been done on the basis of its efficiency.

References

1. Teresa To, Sanja Stanojevic, Ginette Moores, Andrea S Gershon, Eric D Bateman, Alvaro A Cruz, Louis-Philippe Boulet, (2012), Global asthma prevalence in adults: findings from the cross-sectional world health survey, BMC Public Health, 12:204.
2. Dormand, J. R. and Prince, P. J., (1980), A family of embedded Runge–Kutta formulae. J. Comp. Appl. Math., 6, 19–26.
3. Robert H. Lim, Lester Kobzik, Morten Dahl, (2010) Risk for Asthma in Offspring of Asthmatic Mothers versus Fathers: A Meta-Analysis.
4. Bousquet, J., Jeffery, P. K., Busse, W. W., Johnson, M. and Vignola, A. M., (2000), Asthma: From broncho constriction to airways inflammation and remodeling, American J. RespirCrit Care Med, 16: 1720-1745.
5. Yawn B. P., (2008), Factors accounting for asthma variability: achieving optimal symptom control for individual patients, Primary Care Respiratory Journal, 17: 138-147.
6. Sally Spencer, Bhabita Mayer, Kate L Bendall and Eric D Bateman, (2007), Validation of a guideline-based composite outcome assessment tool for asthma control, Respiratory Research, 8:26.
7. http://www.mathworks.in/products/matlab/
8. Hargreave F. E. and Parameswaran K., (2006), Asthma, COPD and bronchitis are just components of airway disease, European Respiratory Journal. 28: 264-267.
9. Payne T., (2000), Computer decision support system, Chest, 118:47-52.
10. Ashish Patel, Jyotsna Choubey, Shailendra K. Gupta, M. K. Verma, Q. Rahman, Rajendra Prasad, (2012), Decision Support System for the Diagnosis of Asthma Severity Using Fuzzy Logic, The 2012 IAENG International Conference on Bioinformatics, HongKong, pp- 142-147 March 14-16.
11. Wilamowski, B. M., (2009), Neural network architectures and learning algorithms, Industrial Electronics Magazine, 3(4): 56- 63.
12. Hagen, T. and Menhaj, M., (1994), Training Feedforward Networks with the Marquardt Algorithm, IEEE Transactions on Neural Networks. 5(6).
13. Semra İçer, Sadık Kara, Ayşegül Güven, (2006), Comparison of multilayer perceptron training algorithms for portal venous doppler signals in the cirrhosis disease, Expert System with Applications, 31(2): 406–413.
14. Ping Liu, Juan hua Sua, Qi ming Dong, He jun Li, (2005), Optimization of aging treatment in lead frame copper alloy by intelligent technique, Materials Letters, 59(26): 3337–3342.
15. Wilamowski, B.M., (2009), Neural network architectures and learning algorithms, Industrial Electronics Magazine, 3(4): 56- 63.
16. Jiahao Zeng, Min An, Nigel John Smith, (2012), Application of a fuzzy based decision making methodology to construction project risk assessment, International Journal of Project Management, 25(6): 589–600.
17. M.Vinay Kumar, Sharad Kulkarini, (2012), Tumors Classification using PNN Methods, International Journal of Soft Computing and Engineering (IJSCE), 2(5): 266-268.
18. Othman, M. F., Basri, M. A. M., (2011), Probabilistic Neural Network for Brain Tumor Classification, Intelligent System, Modeling and Simulation (ISMS), 136- 138.
19. Ashish Patel, Shailendra K Gupta, Qamar Rehman, M. K. Verma, (2013), Application of Fuzzy Logic in Biomedical Informatics, Journal of Emerging Trends in Computing and Information Sciences, 4(1), 57-62.

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Published

2017-05-06

How to Cite

Patel, A., Verma, M. K., & Rahman, Q. (2017). Intelligent system for diagnosis of asthma severity using ANN. CSVTU International Journal of Biotechnology, Bioinformatics and Biomedical, 1(1), 14–19. https://doi.org/10.30732/ijbbb.20160101003

Issue

Vol. 1 No. 1 (2016): Peer Review

Section

Articles

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