title: Neural network diagnostic system for dengue patients risk classification.
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.
authors: Department of Biomedical Engineering, University of Malaya, Kuala Lumpur, Malaysia. tarig_28@yahoo.com
authors: Tarig
authors: T
authors: Faisal
authors: Mohd Nasir
authors: MN
authors: Taib
authors: Fatimah
authors: F
authors: Ibrahim
mesh_terms: D000465:Algorithms; D001219:Aspartate Aminotransferases / Q000097:blood; D015992:Body Mass Index; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D003715:Dengue / Q000145:classification* / Q000175:diagnosis* / Q000453:epidemiology; D006400:Hematocrit; D006801:Humans; D008954:Models, Biological; D016571:Neural Networks, Computer; D010976:Platelet Count; D018570:Risk Assessment; D012307:Risk Factors; D019595:Severe Dengue / Q000145:classification / Q000175:diagnosis / Q000453:epidemiology; D012737:Sex Factors
chemical_list: D001219:Aspartate Aminotransferases