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

Ibrahim, F. and Faisal, T. and Mohamad Salim, M.I. and Taib, M.N. (2010) Non-invasive diagnosis of risk in dengue patients using bioelectrical impedance analysis and artificial neural network. Medical and Biological Engineering and Computing, 48 (11). pp. 1141-1148. ISSN 0140-0118

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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 mm 3), 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. © 2010 International Federation for Medical and Biological Engineering.

Item Type: Article
Additional Information: Ibrahim, F Faisal, T Salim, M I Mohamad Taib, M N eng Research Support, Non-U.S. Gov't 2010/08/05 06:00 Med Biol Eng Comput. 2010 Nov;48(11):1141-8. doi: 10.1007/s11517-010-0669-z. Epub 2010 Aug 4.
Uncontrolled Keywords: Artificial neural network, Bioelectrical impedance analysis, Classify, Dengue fever, Risk, Severity, Amino acids, Benchmarking, Blood, Electric impedance, Neural networks
Subjects: T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering
Depositing User: Mr Jenal S
Date Deposited: 24 Mar 2014 03:45
Last Modified: 01 Nov 2017 05:46
URI: http://eprints.um.edu.my/id/eprint/9319

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