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

Authors:
Fatimah Ibrahim;Mohd Nasir Taib;Wan Abu Bakar Wan Abas;Chan Chong Guan;Saadiah Sulaiman
Affiliations:
Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia;Faculty of Electrical Engineering, Universiti Teknologi Mara, 40450 Shah Alam, Selangor, Malaysia;Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia;Department of Cardiology, Hospital Pantai Putri Ipoh, 126 Jalan Tambun, 31400 Ipoh, Perak, Malaysia;Damansara Specialist Hospital, 119 Jalan SS 20/10, Damansara Utama, 47400 Petaling Jaya, Selangor, Malaysia
Venue:
Computer Methods and Programs in Biomedicine
Year:
2005

Citing 10
Cited 2

Neural computing: theory and practice

Neural computing: theory and practice
Neurocomputing: foundations of research

Neurocomputing: foundations of research
Introduction to the theory of neural computation

Introduction to the theory of neural computation
Generalization by weight-elimination with application to forecasting

NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Fundamentals of neural networks: architectures, algorithms, and applications

Fundamentals of neural networks: architectures, algorithms, and applications
Neural network fundamentals with graphs, algorithms, and applications

Neural network fundamentals with graphs, algorithms, and applications
Neural network design

Neural network design
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Artificial Neural Systems: Foundations, Paradigms, Applications, and Implementations

Artificial Neural Systems: Foundations, Paradigms, Applications, and Implementations
Weight-elimination neural networks applied to coronary surgery mortality prediction

IEEE Transactions on Information Technology in Biomedicine

A noninvasive intelligent approach for predicting the risk in dengue patients

Expert Systems with Applications: An International Journal
Neural network diagnostic system for dengue patients risk classification

Journal of Medical Systems

Quantified Score

Hi-index	0.00

Visualization

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.