A novel dengue fever (DF) and dengue haemorrhagic fever (DHF) analysis using artificial neural network (ANN)

Ibrahim, F. and Taib, M.N. and Wan Abas, W.A.B. and Guan, C.C. and Sulaiman, S. (2005) A novel dengue fever (DF) and dengue haemorrhagic fever (DHF) analysis using artificial neural network (ANN). Computer Methods and Programs in Biomedicine, 79 (3). pp. 273-281. ISSN 01692607, DOI https://doi.org/10.1016/j.cmpb.2005.04.002.

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Abstract

Dengue fever (DF) is an acute febrile viral disease frequently presented with headache, bone or joint and muscular pains, and rash. A significant percentage of DF patients develop a more severe form of disease, known as dengue haemorrhagic fever (DHF). DHF is the complication of DF. The main pathophysiology of DHF is the development of plasma leakage from the capillary, resulting in haemoconcentration, ascites, and pleural effusion that may lead to shock following defervescence of fever. Therefore, accurate prediction of the day of defervescence of fever is critical for clinician to decide on patient management strategy. To date, no known literature describes of any attempt to predict the day of defervescence of fever in DF patients. This paper describes a non-invasive prediction system for predicting the day of defervescence of fever in dengue patients using artificial neural network. The developed system bases its prediction solely on the clinical symptoms and signs and uses the multilayer feed-forward neural networks (MFNN). The results show that the proposed system is able to predict the day of defervescence in dengue patients with 90 prediction accuracy. © 2005 Elsevier Ireland Ltd. All rights reserved.

Item Type:	Article
Funders:	UNSPECIFIED
Additional Information:	Ibrahim, Fatimah Taib, Mohd Nasir Abas, Wan Abu Bakar Wan Guan, Chan Chong Sulaiman, Saadiah eng Research Support, Non-U.S. Gov't Ireland 2005/06/01 09:00 Comput Methods Programs Biomed. 2005 Sep;79(3):273-81.
Uncontrolled Keywords:	Artificial neural networks, Defervescence, Dengue haemorrhagic fever, Prediction model, Bone, Feedforward neural networks, Multilayer neural networks, Neural networks, Pathology, Patient monitoring, Physiology, Recurrent neural networks, Dengue haemorrhagic fever (DHF), Patient management, Disease control, accuracy, article, artificial neural network, ascites, capillary leak syndrome, dengue, hemoconcentration, human, major clinical study, plasma, pleura effusion, positive feedback, prediction, school child, statistical significance, Dengue Hemorrhagic Fever, Humans, Neural Networks (Computer)
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 03:33
Last Modified:	09 Aug 2018 08:58
URI:	http://eprints.um.edu.my/id/eprint/9322

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