Reasearch Awards nomination

Email updates

Keep up to date with the latest news and content from IJHG and BioMed Central.

Open Access Research

Incorporating geographical factors with artificial neural networks to predict reference values of erythrocyte sedimentation rate

Qingsheng Yang12*, Kevin M Mwenda3 and Miao Ge4

Author Affiliations

1 School of Resources and Environment, Guangdong University of Business Studies, Guangzhou, Guangdong, China

2 Department of Geography, University of Lethbridge, Lethbridge, AB, Canada

3 Department of Geography, University of California, Santa Barbara, CA, USA

4 Department of Geography, Shaanxi Normal University, Shaanxi, China

For all author emails, please log on.

International Journal of Health Geographics 2013, 12:11  doi:10.1186/1476-072X-12-11

Published: 12 March 2013

Abstract

Background

The measurement of the Erythrocyte Sedimentation Rate (ESR) value is a standard procedure performed during a typical blood test. In order to formulate a unified standard of establishing reference ESR values, this paper presents a novel prediction model in which local normal ESR values and corresponding geographical factors are used to predict reference ESR values using multi-layer feed-forward artificial neural networks (ANN).

Methods and findings

Local normal ESR values were obtained from hospital data, while geographical factors that include altitude, sunshine hours, relative humidity, temperature and precipitation were obtained from the National Geographical Data Information Centre in China.

The results show that predicted values are statistically in agreement with measured values. Model results exhibit significant agreement between training data and test data. Consequently, the model is used to predict the unseen local reference ESR values.

Conclusions

Reference ESR values can be established with geographical factors by using artificial intelligence techniques. ANN is an effective method for simulating and predicting reference ESR values because of its ability to model nonlinear and complex relationships.

Keywords:
ESR; Geographical factors; Artificial Neural Network; Back propagation