Adaptive Neuro-Fuzzy Inference System for diagnosis risk in dengue patients

Faisal, T. and Taib, M.N. and Ibrahim, F. (2012) Adaptive Neuro-Fuzzy Inference System for diagnosis risk in dengue patients. Expert Systems with Applications, 39 (4). pp. 4483-4495. ISSN 0957-4174

[img]
Preview
PDF (Adaptive Neuro-Fuzzy Inference System for diagnosis risk in dengue patients)
Adaptive_Neuro-Fuzzy_Inference_System_for_diagnosis_risk_in_dengue_patients.pdf - Other

Download (205kB)
Official URL: http://www.scopus.com/inward/record.url?eid=2-s2.0...

Abstract

Dengue disease is considered as one of the life threatening disease that has no vaccine to reduce its case fatality. In clinical practice the case fatality of dengue disease can be reduced to 1 if the dengue patients are hospitalized and prompt intravenous fluid therapy is administrated. Yet, it has been a great challenge to the physicians to decide whether to hospitalize the dengue patients or not due to the overlapping of the medical diagnosis criteria of the disease. Beside that physicians cannot decide to admit all patients because this will have major impact on health care cost saving due to the huge incident of dengue disease in the country. Even if the physicians managed to identify the critical cases to be hospitalized, most of the tools that have been used for monitoring those patients are invasive. Therefore, this study was conducted to develop a non-invasive accurate diagnostic system that can assist the physicians to diagnose the risk in dengue patients and therefore attain the correct decision. Bioelectrical Impedance Analysis measurements, Symptoms and Signs presented with dengue patients were incorporated with Adaptive Neuro-Fuzzy Inference System (ANFIS) to construct two diagnostic models. The first model was developed by systematically optimizing the initial ANFIS model parameters while the second model was developed by employing the subtractive clustering algorithm to optimize the initial ANFIS model parameters. The results showed that the ANFIS model based on subtractive clustering technique has superior performance compared with the other model. Overall diagnostic accuracy of the proposed system is 86.13 with 87.5 sensitivity and 86.7 specificity. © 2011 Elsevier Ltd. All rights reserved.

Item Type: Article
Additional Information: 882SH Times Cited:1 Cited References Count:28
Uncontrolled Keywords: Adaptive Neuro-Fuzzy Inference System, Bioelectrical Impedance Analysis, Dengue fever, Diagnostic system, Risk, Subtractive clustering, Diagnostic systems, Accidents, Clustering algorithms, Disease control, Electric impedance, Fuzzy inference, Fuzzy systems, Health risks, Optimization, Patient treatment, Risks, Diagnosis
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: 17 Feb 2014 08:15
Last Modified: 01 Nov 2017 05:58
URI: http://eprints.um.edu.my/id/eprint/9250

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year