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

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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.

METHODS AND MATERIALS

Figure 1 The ANN model for training and predicting reference ESR values

Figure 1: The ANN model for training and predicting reference ESR values

The input layer has five neurons corresponding to the five geographical factors chosen for the study. There are 5 neurons in the hid- den layer (Figure 1). The output layer has only one neuron which indicates normal ESR value. There are 25 (5×5) weights to be determined for the links between the input layer and the hidden layer, and 5 weights between the hidden layer and output layer. Consequently, a total of 30 parameters are used for the neural network model.

RESULTS AND DISCUSSION

Figure 2 Comparison of ANN regression among Training data, Validation data, Test data and all data

Figure 2: Comparison of ANN regression among Training data, Validation data, Test data and all data

The comparison of ANN regression among training data, validation data, test data and all data are respectively shown in Figure 2. The regression coefficient of test data and all data is higher than training data, which indicates that the trained ANN model is reliable as well. These figures also show the predicted values are very close to measured values with minimal error.

CONCLUSIONS

In this study, a multi-layer feed-forward neural network model has been developed to predict the local reference ESR values, taking into account corresponding local geographical factors. The network is trained using training data, after which the latter is then used to predict unseen reference ESR values. The results show that predicted values are in statistical agreement to measured values.

The use of ANN is an effective method for simulating and predicting reference ESR values because of its ability to model nonlinear and complex relationships. The main advantage of our proposed method is the simplicity and stability of the model structure. Although the reference ESR values can be predicted with geographical factors by using artificial neural networks, the reason why reference ESR values vary with geographical factors should be further explored.

Source: Guangdong University
Authors: Qingsheng Yang | Kevin M Mwenda | Miao Ge

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