Reexamination of risk criteria in dengue patients using the self-organizing map

Even though the World Health Organization criteria’s for classifying the dengue infection have been used for long time, recent studies declare that several difficulties have been faced by the clinicians to apply these criteria. Accordingly, many studies have proposed modified criteria to identify the risk in dengue patients based on statistical analysis techniques. None of these studies utilized the powerfulness of the self-organized map (SOM) in visualizing, understanding, and exploring the complexity in multivariable data. Therefore, this study utilized the clustering of the SOM technique to identify the risk criteria in 195 dengue patients. The new risk criteria were defined as: platelet count less than or equal 40,000 cells per mm3, hematocrit concentration great than or equal 25% and aspartate aminotransferase (AST) rose by fivefold the normal upper limit for AST/alanine aminotransfansferase (ALT) rose by fivefold the normal upper limit for ALT. The clusters analysis indicated that any dengue patient fulfills any two of the risk criteria is consider as high risk dengue patient.

[1]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  G. Crane Dengue haemorrhagic fever: diagnosis, treatment, prevention and control , 1999 .

[3]  M. Datta,et al.  Predictors of spontaneous bleeding in Dengue , 2004, Indian journal of pediatrics.

[4]  M Narayanan,et al.  Dengue Fever - Clinical and Laboratory Parameters Associated with Complications , 2003 .

[5]  T. Monath,et al.  Dengue: the risk to developed and developing countries. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Junbai Wang,et al.  Clustering of the SOM easily reveals distinct gene expression patterns: results of a reanalysis of lymphoma study , 2002, BMC Bioinformatics.

[7]  Akira Igarashi,et al.  Comparison of clinical features and hematologic abnormalities between dengue fever and dengue hemorrhagic fever among children in the Philippines. , 2005, The American journal of tropical medicine and hygiene.

[8]  D. Gubler,et al.  Dengue and Dengue Hemorrhagic Fever , 1998, Clinical Microbiology Reviews.

[9]  E. Arsuaga Uriarte,et al.  Topology Preservation in SOM , 2008 .

[10]  P. W. Chan,et al.  Risk factors for hemorrhage in severe dengue infections. , 2002, The Journal of pediatrics.

[11]  J. Nazuno Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[12]  K. Hatonen,et al.  Advanced analysis methods for 3G cellular networks , 2005, IEEE Transactions on Wireless Communications.

[13]  S. Hales,et al.  Potential effect of population and climate changes on global distribution of dengue fever: an empirical model , 2002, The Lancet.

[14]  Tom Solomon,et al.  Clinical diagnosis and assessment of severity of confirmed dengue infections in Vietnamese children: is the world health organization classification system helpful? , 2004, The American journal of tropical medicine and hygiene.

[15]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[16]  Esa Alhoniemi,et al.  Clustering of the self-organizing map , 2000, IEEE Trans. Neural Networks Learn. Syst..

[17]  Wan Abu Bakar Wan Abas,et al.  A new approach to classify risk in dengue infection using bioelectrical impedance analysis , 2007 .

[18]  Duane J. Gubler,et al.  Chapter 72 – Dengue and Dengue Hemorrhagic Fever , 2006 .

[19]  Iván Machón González,et al.  Self-organizing map and clustering for wastewater treatment monitoring , 2004, Eng. Appl. Artif. Intell..

[20]  Fatimah Ibrahim,et al.  A novel approach to classify risk in dengue hemorrhagic fever (DHF) using bioelectrical impedance analysis (BIA) , 2005, IEEE Transactions on Instrumentation and Measurement.

[21]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[22]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[23]  D. Gubler Epidemic dengue/dengue hemorrhagic fever as a public health, social and economic problem in the 21st century. , 2002, Trends in microbiology.

[24]  Fatimah Binti Ibrahim Fatimah Ibrahim, 2005, Prognosis of dengue fever and dengue haemorrhagic fever using bioelectric impedance, Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur , 2005 .

[25]  F. Ibrahim,et al.  Prognosis of Dengue Fever and Dengue Haemorrhagic Fever using Bioelectrical Impedance , 2005 .

[26]  Taweewong Tantracheewathorn,et al.  Risk factors of dengue shock syndrome in children. , 2007, Journal of the Medical Association of Thailand = Chotmaihet thangphaet.

[27]  Esa Alhoniemi,et al.  Self-Organizing Map for Data Mining in MATLAB: The SOM Toolbox , 1999 .

[28]  A. Nisalak,et al.  Early clinical and laboratory indicators of acute dengue illness. , 1997, The Journal of infectious diseases.

[29]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .

[30]  N. Ismail,et al.  Modeling of hemoglobin in dengue fever and dengue hemorrhagic fever using bioelectrical impedance. , 2004, Physiological measurement.

[31]  John A. Hartigan,et al.  Clustering Algorithms , 1975 .

[32]  Bart Baesens,et al.  Failure prediction with self organizing maps , 2006, Expert Syst. Appl..

[33]  A. Kroeger,et al.  Classifying dengue: a review of the difficulties in using the WHO case classification for dengue haemorrhagic fever , 2006, Tropical medicine & international health : TM & IH.