Skip to main content

Advertisement

SpringerLink
Log in

Non-invasive diagnosis of risk in dengue patients using bioelectrical impedance analysis and artificial neural network

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

This paper presents a new approach to diagnose and classify early risk in dengue patients using bioelectrical impedance analysis (BIA) and artificial neural network (ANN). A total of 223 healthy subjects and 207 hospitalized dengue patients were prospectively studied. The dengue risk severity criteria was determined and grouped based on three blood investigations, namely, platelet (PLT) count (less than or equal to 30,000 cells per mm3), hematocrit (HCT) (increase by more than or equal to 20%), and either aspartate aminotransferase (AST) level (raised by fivefold the normal upper limit) or alanine aminotransferase (ALT) level (raised by fivefold the normal upper limit). The dengue patients were classified according to their risk groups and the corresponding BIA parameters were subsequently obtained and quantified. Four parameters were used for training and testing the ANN which are day of fever, reactance, gender, and risk group’s quantification. Day of fever was defined as the day of fever subsided, i.e., when the body temperature fell below 37.5°C. The blood investigation and the BIA data were taken for 5 days. The ANN was trained via the steepest descent back propagation with momentum algorithm using the log-sigmoid transfer function while the sum-squared error was used as the network’s performance indicator. The best ANN architecture of 3-6-1 (3 inputs, 6 neurons in the hidden layer, and 1 output), learning rate of 0.1, momentum constant of 0.2, and iteration rate of 20,000 was pruned using a weight-eliminating method. Eliminating a weight of 0.05 enhances the dengue’s prediction risk classification accuracy of 95.88% for high risk and 96.83% for low risk groups. As a result, the system is able to classify and diagnose the risk in the dengue patients with an overall prediction accuracy of 96.27%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Annual Report, WHO Collaborating and Research (1998) Dengue haemorrhagic fever. Department of Medical Microbiology, Kuala Lumpur, Malaysia

  2. Balmaseda A et al (2006) Serotype-specific differences in clinical manifestations of dengue. Am J Trop Med Hyg 3(74):449–456

    Google Scholar 

  3. Basheer IA, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 43:3–31

    Article  CAS  PubMed  Google Scholar 

  4. Chua MN, Molanida R, De Guzman M, Laberiza F (1993) Prothrombin time and Partial thromboplastin time as a predictor of bleeding in patient with DHF. Southeast Asian J Trop Med Public Health

  5. Chungue E, Boutin JP, Roux J (1989) Significance of IgM titration by an immunoenzyme technic for the serodiagnosis and epidemiological surveillance of dengue in French Polynesia. Res Virol 3(140):229–240

    Article  Google Scholar 

  6. Faisal T, Taib MN, Ibrahim F (2010) Reexamination of risk criteria in dengue patients using the self-organizing map. Med Biol Eng Comput 48:293–301

    Article  PubMed  Google Scholar 

  7. Faisal T, Taib MN, Ibrahim F (2010) A noninvasive intelligent approach for predicting the risk in dengue patients. Expert Syst Appl 37:2175–2181

    Article  Google Scholar 

  8. Faisal T, Taib MN, Ibrahim F (2010) Neural network diagnostic system for dengue patients risk classification. J Med Syst (in press)

  9. Fang R et al (1982) The dengue epidemic in Malaysia. Epidemiological, serological and virological aspects. Southeast Asian J Trop Med Public Health 1984:15–51

    Google Scholar 

  10. Goh YS, Tan EC (1994) Pruning neural networks during training by backpropagation. TENCON 94. IEEE region 10’s ninth annual international conference

  11. Gubler DJ (1998) Dengue and dengue hemorrhagic fever. Clin Microbiol Rev 11:480–496

    CAS  PubMed  Google Scholar 

  12. Gubler DJ (2002) Epidemic dengue/dengue hemorrhagic fever as a public health, social and economic problem in the 21st century. Trends Microbiol 10:100–103

    Article  CAS  PubMed  Google Scholar 

  13. Hales S, de Wet N, Maindonald J, Woodward A (2002) Potential effect of population and climate changes on global distribution of dengue fever: an empirical model. Lancet 360:830–834

    Article  PubMed  Google Scholar 

  14. Halstead BS (1989) Antibody, macrophages, dengue virus infection, shock, and hemorrhagic: a pathogenetic cascade. Rev Infect Dis 11:830–839

    Google Scholar 

  15. Harris E et al (2000) Clinical, epidemiologic, and virologic features of dengue in the 1998 epidemic in Nicaragua. Am J Trop Med Hyg 63:5–11

    CAS  PubMed  Google Scholar 

  16. Haselsteiner E, Pfurtscheller G (2000) Using time-dependent neural networks for EEG classification. IEEE Trans Rehabil Eng 8:457–463

    Article  CAS  PubMed  Google Scholar 

  17. Haykin S, Neural networks (1994) A comprehensive foundation. Macmillan, New York

    Google Scholar 

  18. Ibrahim F (2005) Prognosis of dengue fever and dengue hemorrhagic fever using bioelectrical impedance. Ph.D. Thesis, Department of Biomedical Engineering, Faculty of Engineering, University of Malaya

  19. Ibrahim F, Ismail NS, Taib MN, Wan Abas WAB (2004) Modeling of hemoglobin in dengue fever and dengue hemorrhagic fever using bioelectrical impedance. Physiol Meas 25:607–615

    Article  CAS  PubMed  Google Scholar 

  20. Ibrahim F, Taib MN, Wan Abas WAB, Chan CG, Sulaiman S (2005) A novel approach to classify risk in dengue hemorrhagic fever (DHF) using bioelectrical impedance. IEEE Trans Instrum Meas 54(1):237–244

    Article  Google Scholar 

  21. Ibrahim F, Taib MN, Wan Abas WAB, Chan CG, Sulaiman S (2005) A novel dengue fever (DF) dengue and hemorrhagic fever (DHF) analysis using artificial neural network. Computer Methods Programs Biomed 79:273–281

    Article  Google Scholar 

  22. Ibrahim F, Taib MN, Wan Abas WAB, Chan CG, Sulaiman S (2008) A new approach to classify risk in dengue infection using bioelectrical impedance analysis (BIA). World Health Org Dengue Bull 31:58–74

    Google Scholar 

  23. Jaffrin MY, Morel H (2009) Extracellular volume measurements using bioimpedance spectroscopy-Hanai method and wrist-ankle resistance at 50 kHz. Med Biol Eng Comput 47:77–84

    Article  PubMed  Google Scholar 

  24. Jaffrin MY, Fenech M, Moreno MV, Kieffer R (2006) Total body water measurement by a modification of the bioimpedance spectroscopy method. Med Biol Eng Comput 44(10):873–882

    Article  PubMed  Google Scholar 

  25. John T, Wei ZZ, Barnhill SD, Madyastha R (1998) Understanding artificial neural networks and exploring their potential applications for the practicing urologist. Urology 52:161–172

    Article  Google Scholar 

  26. Kuo CH, Tai DI, Chang-Chien CS, Lan CK, Chiou SS, Liaw YF (1992) Liver biochemical test and dengue fever. Am J Trop Med Hyg 3(47):265–270

    Google Scholar 

  27. Lam SK, Devi S, Pang T (1987) Detection of specific IgM in dengue infection. Southeast Asian J Trop Med Public Health 4(18):532–538

    Google Scholar 

  28. Ministry of Health Malaysia (2009). http://www.moh.gov.my/MohPortal/newsFull.jsp?action=load&id=401. Assessed 5 Jul 2009

  29. Monath TP (1994) Dengue: the risk to developed and developing countries. Proc Natl Acad Sci USA 91:2395–2400

    Article  CAS  PubMed  Google Scholar 

  30. More FW (1904) Observations on dengue fever in Singapore. J Malaya Branch Br Med Assoc 1:24–29

    Google Scholar 

  31. Negnevitsky M (2002) Artificial intelligence, a guide to intelligent system. First Edition, Pearson education. ISBN: 0201711591

  32. Nimmannitya S (1987) Clinical spectrum and management of dengue haemorrhagic fever. Southeast Asian J Trop Med Public Health 18:392–397

    CAS  PubMed  Google Scholar 

  33. Paterno AS, Stiz RA, Bertemes-Filho P (2009) Frequency-domain reconstruction of signals in electrical bioimpedance spectroscopy. Med Biol Eng Comput 47(10):1093–1102

    Article  PubMed  Google Scholar 

  34. Rebecca George MD (1992) Current status of the knowledge of dengue/DHF/DSS in Malaysia: clinical aspects. 15th Annual Convention of the Philippine Society foe Microbiology and Infectious Diseases

  35. Rigaud B, Morucci JP, Chauveau N (1996) Bioelectrical impedance techniques in medicine part I: bioimpedance measurement second section: impedance spectrometry. In: Bourne JR (ed) Critical reviews in biomedical engineering. Begell House, New York, vol 24

  36. Rudnick A et al (1965) Mosquito borne haemorrhagie fever in Malaysia. Br Med J 1:1269–1272

    Article  CAS  PubMed  Google Scholar 

  37. Sinha SK, Fieguth PW (2005) Projection neural network model for classification of pipe defects. J Autom Constr 15(1):73

    Article  Google Scholar 

  38. Sun M, Sclabassi RJ (2000) The forward EEG solutions can be computed using artificial neural networks. IEEE Trans Biomed Eng 47:1044–1050

    Article  CAS  PubMed  Google Scholar 

  39. Wallace Hazel G et al (1980) Dengue haemorthagic fever in Malaysia 1973 epidemic. Southeast Asian J Trop Med Public Health 11:1–12

    Google Scholar 

  40. Weigend AS, Rumelhart DE, Huberman BA (1991) Generalization by weight elimination with applications to forecasting. In: Lippmann R, Moody J, Touretzky D (eds) Advances in neural information processing 3, pp 875–882

  41. World Health Organization (1997) Dengue haemorrhagic fever: diagnosis, treatment, prevention and control, 2nd edn. WHO, Geneva

    Google Scholar 

Download references

Acknowledgments

This research was supported by University of Malaya, Sultan Iskandar Johore Foundation, and Ministry of Science, Technology and Innovation (MOSTI) (E-Science Fund 11-02-1014).

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia

    F. Ibrahim, T. Faisal & M. I. Mohamad Salim

  2. Faculty of Electrical Engineering, Universiti Teknologi Mara, 40450, Shah Alam, Selangor, Malaysia

    M. N. Taib

Authors
  1. F. Ibrahim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. T. Faisal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. I. Mohamad Salim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. N. Taib
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to F. Ibrahim.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ibrahim, F., Faisal, T., Mohamad Salim, M.I. et al. Non-invasive diagnosis of risk in dengue patients using bioelectrical impedance analysis and artificial neural network. Med Biol Eng Comput 48, 1141–1148 (2010). https://doi.org/10.1007/s11517-010-0669-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-010-0669-z

Keywords

Search

Navigation