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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2013

Open Access 01-12-2013 | Research article

Artificial neural network models for prediction of cardiovascular autonomic dysfunction in general Chinese population

Authors: Juanmei Liu, Zi-Hui Tang, Fangfang Zeng, Zhongtao Li, Linuo Zhou

Published in: BMC Medical Informatics and Decision Making | Issue 1/2013

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Abstract

Background

The present study aimed to develop an artificial neural network (ANN) based prediction model for cardiovascular autonomic (CA) dysfunction in the general population.

Methods

We analyzed a previous dataset based on a population sample consisted of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN analysis. Performances of these prediction models were evaluated in the validation set.

Results

Univariate analysis indicated that 14 risk factors showed statistically significant association with CA dysfunction (P < 0.05). The mean area under the receiver-operating curve was 0.762 (95% CI 0.732–0.793) for prediction model developed using ANN analysis. The mean sensitivity, specificity, positive and negative predictive values were similar in the prediction models was 0.751, 0.665, 0.330 and 0.924, respectively. All HL statistics were less than 15.0.

Conclusion

ANN is an effective tool for developing prediction models with high value for predicting CA dysfunction among the general population.
Appendix
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Metadata
Title
Artificial neural network models for prediction of cardiovascular autonomic dysfunction in general Chinese population
Authors
Juanmei Liu
Zi-Hui Tang
Fangfang Zeng
Zhongtao Li
Linuo Zhou
Publication date
01-12-2013
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2013
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/1472-6947-13-80

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