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ASME Press Select Proceedings
Intelligent Engineering Systems through Artificial Neural Networks Volume 18
Editor
Cihan H. Dagli
Cihan H. Dagli
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ISBN-10:
0791802823
ISBN:
9780791802823
No. of Pages:
700
Publisher:
ASME Press
Publication date:
2008

Syndromic Surveillance is used for the early detection of disease outbreaks. Back Propagation has been demonstrated to be an accurate, robust, and scalable detection technique for disease outbreaks in over the counter pharmaceutical sales. The purpose of this study is to determine whether Support Vector Machines are comparable to Back Propagation in performing Syndromic Surveillance. Back Propagation and Support Vector Machines are used to detect outbreaks based on emergency department and Telehealth data. A data simulation methodology has been used to produce sufficient quantities of realistic data to perform this study. The results demonstrated that Support Vector Machines with a polynomial kernel are more successful to Back Propagation for detecting disease outbreaks based on data from emergency department in terms of false detections; however, they are comparable in terms of the detection time. In addition, Support Vector Machines with a polynomial kernel are comparable to Back Propagation for detecting outbreaks based on data from Telehealth in terms of false detections and the detection time.

Abstract
Introduction
Data Sources
Method
Data Simulation
Parameters of Back Propagation
Parameters of Support Vector Machine
Output Values
Normalization
Determining Threshold Levels
Performance Measures
Results and Analysis
Normality Test and Statistical Approach
Significance Level
Analysis of Results
Statistical Analysis of TTD Results
Statistical Analysis of FN Results
Statistical Analysis of FP Results
Conclusions
References
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