Int J Health Geogr - Incorporating geographical factors with artificial neural networks to predict reference values of erythrocyte sedimentation rate.

Tópicos

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Resumo

CKGROUND: 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.

Resumo Limpo

ckground measur erythrocyt sediment rate esr valu standard procedur perform typic blood test order formul unifi standard establish refer esr valu paper present novel predict model local normal esr valu correspond geograph factor use predict refer esr valu use multilay feedforward artifici neural network annmethod find local normal esr valu obtain hospit data geograph factor includ altitud sunshin hour relat humid temperatur precipit obtain nation geograph data inform centr chinath result show predict valu statist agreement measur valu model result exhibit signific agreement train data test data consequ model use predict unseen local refer esr valuesconclus refer esr valu can establish geograph factor use artifici intellig techniqu ann effect method simul predict refer esr valu abil model nonlinear complex relationship

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