Neural network ensemble model for prediction of erythrocyte sedimentation rate (ESR) using partial least squares regression

Abstract

The erythrocyte sedimentation rate (ESR) is a non-specific blood test for determining inflammatory conditions. However, the long measurement time (60 min) to obtain ESR is an obstacle for a prompt evaluation. In this study, to reduce the measurement time of ESR, deep neural networks (DNNs) were applied to the sedimentation tendency of blood samples. DNNs using multilayer perceptron (MLP), long short-term memory (LSTM), and gated recurrent unit (GRU) were assessed and compared to determine a suitable length of time for the input sequence. To avoid overfitting, a stacking ensemble learning was adopted, which combines multiple models by using a meta model. Four meta models were compared: mean, median, least absolute shrinkage and selection operator, and partial least squares regression (PLSR) schemes. From the empirical results, LSTM and GRU models have better prediction than MLP over sequence lengths of 5 to 20 min. The decrease in $$\overline{\mathrm{MAPE} }$$ MAPE ¯ and $$\overline{\mathrm{RMSE} }$$ RMSE ¯ of GRU and LSTM was attenuated after a sequence length of 15 min, so the input sequence length is determined as 15 min. In terms of the meta model, the statistical comparison suggests that GRU combined with PLSR (GRU–PLSR) is the best case. Then, the GRU–PLSR was tested for prediction of ESR data obtained from periodontitis patients to check its applicability to a specific disease. The Bland–Altman plot shows acceptable agreement between measured and predicted ESR values. Based on the results, the GRU–PLSR can predict ESR with improved performance within 15 min and has potential applicability to ESR data with inflammatory and non-inflammatory conditions.