Abstract
This paper presents the determination of the bioimpedance analysis parameters in dengue infection using the self organizing map. The self organizing map (SOM) was used for visualizing, understanding and exploring the significant bioimpedance analysis (BIA) parameters that can distinguish between dengue patients and the healthy patients. Database of 329 data set (203 females and 126 males) were used in this study. The investigation was conducted on the day of defervescence of fever. The BIA parameters, which are comprised of Resistance, Reactance, Phase Angle, Body Capacitance, Body Cell Mass, Extracellular Mass, Fat Mass, Body Mass Index, Basal Metabolic Rate, Total Body Water, Intracellular Water, Extracellular Water, Lean body mass, and weight are used. Three bars of training were conducted. The first training was conducted using all the data. The best map size was found as 100 units. Second training was conducted based on the female’s data. The best map size was found as 72 units. Finally, 70 units SOM was obtained when the male’s data was used. Moreover, significant results were found by visualizing the three trained maps. The SOM showed that the reactance is significantly low in dengue patients when the all data was used. However, when the data was analyzed separately for females and males, the SOM showed that the Intracellular Water is significantly low while the ratio of the Extracellular Water and Intracellular Water are significantly high in both males and females. Moreover, the SOM showed that the reactance is significantly high while the ratio of the Extracellular Mass and Body Cell Mass is significantly low for females.
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VII. References
Monath TP. (1994) Dengue: the risk to developed and developing countries. Proc. Natl Acad Sci. 91, USA, 1994, pp 2395–2400
Gubler DJ. (2002) Epidemic dengue/dengue hemorrhagic fever as a public health, social and economic problem in the 21st century. Trends Microbiol. 10, pp 100–103
World Health Organization (1997) Dengue Haemorrhagic Fever: Diagnosis, Treatment, Prevention and Control. 2nd ed., Geneva.
Ibrahim F, Taib M.N, Wan Abas W.A.B et al. (2005) A Novel Approach to Classify Risk in Dengue Hemorrhagic fever (DHF) using Bioelectrical Impedance Analysis. IEEE transactions of instrumentation and measurement, vol. 54, pp 237–244
Ibrahim F, Taib M.N, Wan Abas W.A.B et al. (2005) A Novel Dengue fever (DF) Dengue and Hemorrhagic fever (DHF) Analysis using artificial neural network. Computer methods and programs in biomedicine, vol. 79, pp 273–281
Lampinen L, Oja E (1995) Distortion tolerant pattem recognition based on self-organizing feature extraction. IEEE transactions of Neural Network, vol. 6, pp 539–547
Kohonen T, Makisara K, Saramaki T (1984) Phonotopic maps-Insightful representation of phonological features for speech recognition. Proc. 7ICPR, IEEE Computer Soc. Press, pp 182–185.
Heikkonen J, Koikkalainen P, Oja E, (1993) Self-organizing maps for collision-free navigation. Proc. World Congress on Neur. Networks, vol. 3, Portland, 1993, pp 141–144
Ansari N, Chen Y (1990) A neural network model to configure maps for a satellite communication network. Proc. GLOBECOM’90, vol. 2, IEEE Global Telecommun. Con and Exhibit: Communications: Connecting the Future, Piscataway, NJ, 1990, pp 1042–1046
Christodoulos I, Constantinos. S, Pattichis (1999) Medical Diagnostic Systems using Ensembles of Neural SOFM Classifiers. IEEE 1999
Esa Alhoniemi, Jaakko Hollmen, Olli Simula et al. (1999) Process Monitoring and Modeling Using the Self-Organizing Map. Integrated Computer-Aided Engineering, vol. 6, pp 3–14
Sebelius F, Eriksson L, Holmberg H et al. (2005) Classification of motor commands using a modified self-organising feature map. Medical Engineering & Physics, vol. 27, pp 403–413
Janne Nikkila, Petri Toronen, Samuel Kaski et al. (2002) Analysis and visualization of gene expression data using Self-Organizing Maps. Neural Networks, vol. 15, pp 953–966
Haykin S (1999) Neural Networks: A Comprehensive Foundation. Prentice Hall, 2nd ed., New Jersey
Kohonen T (1990) The self-organizing map. Proc. IEEE, vol. 78, pp 1464–1480
Arsuaga Uriarte E, Diaz Martin F (2004) Topology Preservation in SOM. International journal of applied mathematics and computer sciences, vol. 1, ISSN 1307-6906
Kohonen T (1997) Self-Organizing Maps, 2nd ed. Springer-Verlag, Berlin
Jing Li, Information Visualization with Self-Organizing Maps at http://www14.in.tum.de/konferenzen/Jass05/courses/6/Papers/09.pdf
Ibrahim F, (2005) Prognosis of Dengue Fever and Dengue Hemorrhagic Fever Using Bioelectrical Impedance. Ph.D thesis. University of Malaya
Oxford University Press, Concise medical dictionary, 3rd ed., Oxford, 1990
Biodynamics Model 450 Bioimpedance Analyzer user’s guide, Basic Principles of Bioimpedance Testing, First edition, copyright© Biodynamics Corporation
Ibrahim F, N.A Ismail, M.N Taib et al. (2004) Modeling of hemoglobin in dengue fever and dengue hemorrhagic fever using bioelectrical impedance. Physiol. Meas., vol. 25, pp. 607–616
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Faisal, T., Ibrahim, F., Taib, M.N. (2008). Determination of the Bioimpedance Analysis Parameters in Dengue Patients Using the Self Organizing Map. In: Abu Osman, N.A., Ibrahim, F., Wan Abas, W.A.B., Abdul Rahman, H.S., Ting, HN. (eds) 4th Kuala Lumpur International Conference on Biomedical Engineering 2008. IFMBE Proceedings, vol 21. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69139-6_46
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DOI: https://doi.org/10.1007/978-3-540-69139-6_46
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