Adaptive Neuro-Fuzzy Inference System for diagnosis risk in dengue patients

Expert Systems with Applications

Volumn 39, Issue 4, 2012, Pages 4483-4495

  Faisal, Tarig ^a   Taib, Mohd Nasir ^b   Ibrahim, Fatimah ^a  

^a UNIVERSITY OF MALAYA   (Malaysia)

^b UNIVERSITI TEKNOLOGI MARA   (Malaysia)

Author keywords

Adaptive Neuro Fuzzy Inference System; Bioelectrical Impedance Analysis; Dengue fever; Diagnostic system; Risk; Subtractive clustering

Indexed keywords

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; BIOELECTRICAL IMPEDANCE ANALYSIS; DENGUE FEVER; DIAGNOSTIC SYSTEMS; SUBTRACTIVE CLUSTERING;

ACCIDENTS; CLUSTERING ALGORITHMS; DISEASE CONTROL; ELECTRIC IMPEDANCE; FUZZY INFERENCE; FUZZY SYSTEMS; HEALTH RISKS; OPTIMIZATION; PATIENT TREATMENT; RISKS;

DIAGNOSIS;

EID: 82255175496     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2011.09.140     Document Type: Article

References (29)

1. A. Akgundogdu, S. Kurt, N. Kilic, O.N. Ucan, and N. Akalin Diagnosis of renal failure disease using adaptive neuro-fuzzy inference system Journal of Medical Systems 56 2009 10.1007/s10916-009-9317-2
2. B. AL-Bokhity, T.M. Nazmy, and H. EL-Messiry Classification of cardiac arrhythmia based on hybrid International Journal of Computer Systems 2 2010 18 23
3. S. Bandyopadhyay, L.C.S. Lum, and A. Kroeger Classifying dengue: A review of the difficulties in using the WHO case classification for dengue haemorrhagic fever Tropical Medicine and International Health 11 8 2006 1238 1255 (Pubitemid 44125713)
4. L. Benecchi Neuro-fuzzy system for prostate cancer diagnosis Urology 68 2006 357 361 (Pubitemid 44175504)
5. J.W. Catto, M.F. Abbod, D.A. Linkens, and F.C. Hamdy Neuro-fuzzy modelling:neuro-fuzzy modelling: An accurate and interpretable method for predicting bladder cancer progression Journal of Urology 175 2006 474 479 (Pubitemid 43067329)
6. S.L. Chiu Fuzzy model identification based on cluster estimation Journal of Intelligent and Fuzzy Systems 2 1994 267 278
7. D. Delen, R. Sharda, and M. Bessonov Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks Accident Analysis and Prevention 38 3 2006 434 444
8. T. Faisal, F. Ibrahim, and M.N. Taib A noninvasive intelligent approach for predicting the risk in dengue patients Expert Systems with Applications 37 2010 2175 2181
9. T. Faisal, M.N. Taib, and F. Ibrahim Reexamination of risk criteria in dengue patients using the self organizing map Journal Medical and Biological Engineering and Computing 48 3 2010 293 301
10. T. Faisal, M.N. Taib, and F. Ibrahim Neural network diagnostic system for dengue patients risk classification Journal of medical systems 2010 10.1007/s10916-010-9532-x
11. F. Ibrahim, M.N. Taib, W.A.B.W. Abas, Chan Chong Guan, and S. Sulaiman A novel approach to classify risk in dengue hemorrhagic fever (DHF) using bioelectrical impedance analysis IEEE Transaction on Instrumentation and Measurement 54 1 2005 237 244 (Pubitemid 40181658)
12. F. Ibrahim, T. Faisal, M.I. Salim, and M.N. Taib Non-invasive diagnosis of risk in dengue patients using bioelectrical impedance analysis and artificial neural network Medical and Biological Engineering and Computing 48 11 2010 1141 1148
13. F. Ibrahim Prognosis of dengue fever and dengue hemorrhagic fever using bioelectrical impedance 2005 University of Malaya Malaysia
14. J.S.R. Jang, C.T. Sun, and E. Mizutani Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligent 1996 Prentice Hal.
15. J.S.R. Jang ANFIS: Adaptive-network-based fuzzy inference system IEEE Transactions on Systems, Man, and Cybernetics 23 3 1993 665 685
16. M. Khezri, and M. Jahed Real-time intelligent pattern recognition algorithm for surface EMG signals BioMedical Engineering OnLine 6 2007 1 12
17. Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In The fourteenth international joint conference on artificial intelligence (IJCAI), (pp. 1137-1145). Montreal, Quebec, Canada.
18. C.H. Kuo, D.I. Tai, C.S. Chang-Chein, C.K. Lan, S.S. Chiou, and Y.F. Liaw Liver biochemical test and dengue fever The American Journal of Tropical Medicine and Hygiene 47 3 1992 265 270
19. H.F. Kwok, D.A. Linkens, M. Mahfouf, and G.H. Mills Rule-base derivation for intensive care ventilator control using ANFIS Artificial Intelligence in Medicine 29 2003 185 201 (Pubitemid 37491335)
20. R.T. Lauer, B.T. Smith, and R.R. Betz Application of a neuro-fuzzy network for gait event detection using electromyography in the child with cerebral palsy IEEE Transactions on Biomedical 52 2005 1532 1540 (Pubitemid 41462183)
21. MathWorks, Inc. (2007). Fuzzy logic toolbox user's guide.
22. Ministry of Health Malaysia (1974). Annual reports of vector borne disease control programme.
23. Ministry of Health Malaysia (2008). KENYATAAN AKHBAR KPK UNTUK DENGGI DAN CHIKUNGUNYA MINGGU 53 2008.
24. C.F. Ng, L.C. Lum, N.A. Ismail, L.H. Tan, and C.P. Tan Clinicians' diagnostic practice of dengue infections Journal of Clinical Virology 40 3 2007 202 206 (Pubitemid 47552930)
25. C.X.T. Phuong Clinical diagnosis and assessment of severity of confirmed dengue infection in Vietnamese: Is the world health classification system helpful? The American Journal of Tropical Medicine and Hygiene 70 2 2004 172 179 (Pubitemid 38200522)
26. A. Subasi Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction Computers in Biology and Medicine 37 2007 227 244 (Pubitemid 44909087)
27. E.D. Ubeyli, and I. Guler Adaptive neuro-fuzzy inference systems for analysis of internal carotid arterial Doppler signals Computers in Biology and Medicine 35 2005 687 702 (Pubitemid 41206728)
28. World Health Organisation (2009). Dengue hemorrhagic fever: Diagnosis, treatment and control. Geneva.
29. World Health Organization (1997). Dengue haemorrhagic fever: diagnosis, treatment, prevention and control, 2nd ed. Geneva: WHO.