Comparison of adaptive neuro-fuzzy inference system and artificial neutral networks model to categorize patients in the emergency department

SpringerPlus

Volumn 2, Issue 1, 2013, Pages 1-10

  Azeez, Dhifaf ^a   Ali, Mohd Alauddin Mohd ^a   Gan, Kok Beng ^a   Saiboon, Ismail ^b  

^a UNIVERSITI KEBANGSAAN MALAYSIA   (Malaysia)

^b Universiti Kebangsaan ^*  (Malaysia)

Author keywords

Adaptive neuro fuzzy inference system; Emergency medical services; Neural network; Triage

Indexed keywords

EID: 84887296994     PISSN: None     EISSN: 21931801     Source Type: Journal    
DOI: 10.1186/2193-1801-2-416     Document Type: Article

References (41)

1. Akay M (2000) Nonlinear Biomedical Signal Processing: Fuzzy Logic, Neural Networks, and New Algorithms. Wiley-IEEE Press, New York.
2. Andriulli A, Grossi E, Buscema M, Festa V, Intraligi NM, Dominici P, Cerutti R, Perri F (2003) Contribution of artificial neural networks to the classification and treatment of patients with uninvestigated dyspepsia. Dig Liver Dis 35(4):222-231. doi:10.1016/s1590-8658(03)00057-4.
3. Asiltürk İ, Çunkaş M (2011) Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method. Expert Syst Appl 38(5):5826-5832. doi:10.1016/j.eswa.2010.11.041.
4. Benmiloud T (2011) Improved adaptive neuro-fuzzy inference system. Neural Comput Appl:1-8. doi:10.1007/s00521-011-0607-5.
5. Bodyanskiy Y, Dolotov A (2010) Methods and Instruments of Artificial Intelligence. ITHEA, Rzeszow-Sofia, Bulgaria, pp 17-24.
6. Bradley V (1995) Toward a common language: Emergency Nursing Uniform Data Set (ENUDS). J Emerg Nurs 21(3):248-250. doi:10.1016/s0099-1767(05)80171-0.
7. Bradley V (1996) Development of an emergency data set: a worthwhile challenge. J Emerg Nurs 22(3):238-240. doi:10.1016/s0099-1767(96)80120-6.
8. Das A, Ben-Menachem T, Cooper GS, Chak A, Sivak MV, Jr, Gonet JA, Wong RCK (2003) Prediction of outcome in acute lower-gastrointestinal haemorrhage based on an artificial neural network: internal and external validation of a predictive model. Lancet 362(9392):1261-1266. doi:10.1016/s0140-6736(03)14568-0.
9. Gilboy N, Tanabe P, Travers DA (2005) The Emergency Severity Index Version 4: changes to ESI level 1 and pediatric fever criteria. J Emerg Nurs 31(4):357-362. doi:10.1016/j.jen.2005.05.011.
10. Gosztolya G, B'anhalmi A, T'oth L (2009) Using One-Class classsification Techniques in the Anti-phoneme Problem. In: Hélder JA, Ana MM, Armando J (ed) Pattern Recognition and Image Analysis 4th Iberian Conference, IbPRIA 2009 Póvoa de Varzim, Portugal, Proceedings. Springer-Verlag Berlin Heidelberg, New York.
11. Grafstein E, Bullard MJ, Warren D, Unger B (2008) Revision of the Canadian Emergency Department Information System (CEDIS) Presenting Complaint List version 1.1. CJEM 10(2):151-173.
12. Guler I, Ubeyli ED (2005) Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. J Neurosci Methods 148(2):113-121. Epub 2005 Jul.
13. Haykin S (2009) Neural Networks and Learning Machines, 3rd edition. Pearson Education, Inc., New Jersey.
14. Jang J-SR (1991) Fuzzy modeling using generalized neural networks and Kalman filter algorithm. AAAI'91 Proceedings of the ninth National Conference on Artificial Intelligence. AAA1 Press 2:762-767.
15. Jang JSR (1992) Self-learning fuzzy controllers based on temporal backpropagation. IEEE Trans Neural Netw 3(5):714-723.
16. Kermani BG, Schiffman SS, Nagle HT (2005) Performance of the Levenberg-Marquardt neural network training method in electronic nose applications. Sens Actuators B 110(1):13-22. doi:10.1016/j.snb.2005.01.008.
17. Kevin M-J (1997) Emergency triage. BMJ Publishing, London.
18. Kisi Ö (2004) Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation. Hydrol Sci J 49(6):1040. doi:10.1623/hysj.49.6.1025.55720.
19. Klopotek M, Wierzchon S, Trojanowski K (2004) Confusion matrix visualization. Intelligent information processing and web mining. Proceedings of the International IIs: Iipwm'04 Conference Held in Zakopane, Poland, (advances In Intelligent and Soft Computing). Springer, Verlag Berlin, Heidelberg.
20. Lee SMD (2011) Disaster Management and Emergency Medicine in Malaysia. West J Emerg Med 12(1).
21. Lin W-T, Wang S-T, Chiang T-C, Y-x S, W-y C, H-m C (2010) Abnormal diagnosis of Emergency Department triage explored with data mining technology: An Emergency Department at a Medical Center in Taiwan taken as an example. Expert Syst Appl 37(4):2733-2741. doi:10.1016/j.eswa.2009.08.006.
22. Lin WT, Wu YC, Zheng JS, Chen MY (2011) Analysis by data mining in the emergency medicine triage database at a Taiwanese regional hospital. Expert Syst Appl 38(9):11078-11084. doi:10.1016/j.eswa.2011.02.152.
23. Lisboa PJG (2002) A review of evidence of health benefit from artificial neural networks in medical intervention. Neural Netw 15(1):11-39. doi:10.1016/s0893-6080(01)00111-3.
24. Liu J, Yuan X (2009) Obscure bleeding detection in endoscopy images using support vector machines. Optimization and Engineering 10(2):289-299. doi:10.1007/s11081-008-9066-y Springer US.
25. Majumdar K (2011) Review article: Human scalp EEG processing: Various soft computing approaches. Appl Soft Comput 11(8):4433-4447. doi:10.1016/j. asoc.2011.07.004.
26. Mangalampalli A, Mangalampalli SM, Chakravarthy R, Jain AK (2006) A neural network based clinical decision-support system for efficient diagnosis and fuzzy-based prescription of gynecological diseases using omoeopathic medicinal system. Expert Syst Appl 30(1):109-116.
27. Michalowski W, Rubin S, Slowinski R, Wilk S (2003) Mobile clinical support system for pediatric emergencies. Decis Support Syst 36(2):161-176. doi:10.1016/s0167-9236(02)00140-9.
28. Michalowski W, Slowinski R, Wilk S, Farion KJ, Pike J, Rubin S (2005) Design and development of a mobile system for supporting emergency triage. Methods Inf Med 44(1):14-24.
29. Mitra P, Maulik S, Chowdhury SP, Chowdhury S (2007) ANFIS based automatic voltage regulator with hybrid learning algorithm. In: Universities Power Engineering Conference, 2007. UPEC 2007. 42nd International, 4-6 Sept. 2007. IEEE, Brighton, pp 397-401.
30. Moghaddamnia A, Remesan R, Kashani MH, Mohammadi M, Han D, Piri J (2009) Comparison of LLR, MLP, Elman, NNARX and ANFIS Models-with a case study in solar radiation estimation. J Atmos Sol-Terr Phys 71(8-9):975-982. doi:10.1016/j.jastp.2009.04.009.
31. Negnevitsky M (2005) Artificial intelligence: a guide to intelligent systems/Michael Negnevitsky. Addison-Wesley, New York. vol Accessed from http://nla.gov.au/nla.cat-vn3803044.
32. zkan AO, Kara S, Salli A, Sakarya ME, Güneş S (2010) Medical diagnosis of rheumatoid arthritis disease from right and left hand Ulnar artery Doppler signals using adaptive network based fuzzy inference system (ANFIS) and MUSIC method. Adv Eng Softw 41(12):1295-1301. http://dx.doi.org/10.1016/j. advengsoft.2010.10.001.
33. Padmanabhan N, Burstein F, Churilov L, Wassertheil J, Hornblower B, Parker NA (2006) Mobile Emergency Triage Decision Support System Evaluation. In: System Sciences, 2006. HICSS '06. Proceedings of the 39th Annual Hawaii International Conference on, 04-07 Jan. 2006. IEEE Computer Society, Kauai, HI, USA, p 96b.
34. Pandey B, Mishra RB (2009) Knowledge and intelligent computing system in medicine. Comput Biol Med 39(3):215-230.
35. Patterson P, Draper S (1998) A neural net representation of experienced and nonexperienced users during manual wheelchair propulsion. J Rehabil Res Dev 35(1):43-51.
36. Sadeghi S, Barzi A, Sadeghi N, King B (2006) A Bayesian model for triage decision support. Int J Med Inform 75(5):403-411.
37. San Pedro J, Burstein F, Cao P, Churilov L, Zaslavsky A, Wassertheil J (2004) Mobile Decision Support for Triage in Emergency Departments. Decision Support in an Uncertain and Complex World: The IFIP TC8/WG8.3 International Conference, Prato, Italy, pp 714-723.
38. Shuangwen S, Gao RX (2006) Optimization of ANFIS with Applications in Machine Defect Severity Classification. 2006 International Joint Conference on Neural Networks, Sheraton Vancouver Wall Center Hotel, Vancouver, BC, Canada, pp 728-734. July 16-21, 2006.
39. Wilk S, Slowinski R, Michalowski W, Greco S (2005) Supporting triage of children with abdominal pain in the emergency room. Eur J Oper Res 160(3):696-709.
40. Yahya MN, Otsuru T, Tomiku R, Okozono T (2010) Investigation the Capability of Neural Network in Predicting Reverberation Time on Classroom. Int J Sust Constr Eng Technol 1(1):19-31.
41. Zare Nezhad B, Aminian A (2010) A multi-layer feed forward neural network model for accurate prediction of flue gas sulfuric acid dew points in process industries. Appl Therm Eng 30(6-7):692-696. doi:10.1016/j.applthermaleng.2009.11.017.