Improving prediction accuracy of influenza-like illnesses in hospital emergency departments

Influenza poses a significant risk to public health, as evident by the 2009 H1N1 pandemic. Hospital emergency departments monitor infectious diseases such as influenza with surveillance systems based on arriving chief complaints. However, existing systems are too reliant on the completeness of data...

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Bibliographic Details
Published in2013 IEEE International Conference on Bioinformatics and Biomedicine pp. 602 - 607
Main Authors Pei, Jiefu, Ling, Bo, Liao, Simon, Liu, Baiyan, Huang, Jimmy Xiangji, Strome, Trevor, de Faria, R. Lobato, Zhang, Michael G
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2013
Subjects
Accuracy
Artificial neural networks
C4.5 Decision Tree
Decision trees
Entropy
flu prediction
Hospitals
ICI
Influenza
pattern classification
Training
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Summary:Influenza poses a significant risk to public health, as evident by the 2009 H1N1 pandemic. Hospital emergency departments monitor infectious diseases such as influenza with surveillance systems based on arriving chief complaints. However, existing systems are too reliant on the completeness of data and are not acceptably accurate in a practical setting. To improve prediction accuracy, we propose a data cleaning process for data collected in hospital settings. Besides, we also propose a novel feature selection method called the Importance Contribution Index (ICI) which is based on orthogonal transformation. Various feature selection and pattern classification approaches are analyzed. The ICI and C4.5 decision tree are eventually adopted in the new surveillance system. Validation results have shown that the total accuracy has been improved by 7.1% in the enhanced system.
DOI:10.1109/BIBM.2013.6732566