Neural network diagnostic system for dengue patients risk classification

Faisal, T. and Taib, M.N. and Ibrahim, Fatimah (2012) Neural network diagnostic system for dengue patients risk classification. Journal of Medical Systems, 36 (2). pp. 661-676. ISSN 0148-5598

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Abstract

With the dramatic increase of the worldwide threat of dengue disease, it has been very crucial to correctly diagnose the dengue patients in order to decrease the disease severity. However, it has been a great challenge for the physicians to identify the level of risk in dengue patients due to overlapping of the medical classification criteria. Therefore, this study aims to construct a noninvasive diagnostic system to assist the physicians for classifying the risk in dengue patients. Systematic producers have been followed to develop the system. Firstly, the assessment of the significant predictors associated with the level of risk in dengue patients was carried out utilizing the statistical analyses technique. Secondly, Multilayer perceptron neural network models trained via Levenberg-Marquardt and Scaled Conjugate Gradient algorithms was employed for constructing the diagnostic system. Finally, precise tuning for the models' parameters was conducted in order to achieve the optimal performance. As a result, 9 noninvasive predictors were found to be significantly associated with the level of risk in dengue patients. By employing those predictors, 75 prediction accuracy has been achieved for classifying the risk in dengue patients using Scaled Conjugate Gradient algorithm while 70.7 prediction accuracy were achieved by using Levenberg-Marquardt algorithm. © Springer Science+Business Media, LLC 2010.

Item Type: Article
Additional Information: Faisal, Tarig Taib, Mohd Nasir Ibrahim, Fatimah eng 2010/08/13 06:00 J Med Syst. 2012 Apr;36(2):661-76. doi: 10.1007/s10916-010-9532-x. Epub 2010 Jun 25.
Uncontrolled Keywords: Levenberg-Marquardt dengue fever, Multilayer perceptron, Scaled conjugate gradient, article, artificial neural network, classification algorithm, dengue, diagnostic accuracy, diagnostic test accuracy study, disease classification, female, human, male, perceptron, predictor variable, risk assessment, sensitivity and specificity, statistical analysis, algorithm, biological model, blood, body mass, classification, decision support system, hematocrit, organization and management, risk factor, sex difference, thrombocyte count, aspartate aminotransferase, Algorithms, Aspartate Aminotransferases, Body Mass Index, Decision Support Systems, Clinical, Dengue Hemorrhagic Fever, Humans, Models, Biological, Neural Networks (Computer), Platelet Count, Risk Factors, Sex Factors
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: 07 Apr 2014 02:50
Last Modified: 26 Aug 2019 06:41
URI: http://eprints.um.edu.my/id/eprint/9315

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