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Beetle Antennae Search Strategy for Neural Network Model Optimization with Application to Glomerular Filtration Rate Estimation

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Abstract

The determination of weights is very important for neural network models. Nevertheless, the traditional feedforward neural networks usually use the method of random initial values to determine the weights of the neural networks, such a method would make the related neural network models possess unstable performance in accuracy. Therefore, in order to improve the prediction accuracy and efficiency of neural networks, a novel neural network model based on beetle antennae search (BAS) and weights and structure policy (WASP) is proposed and applied to the estimation of glomerular filtration rate (GFR). Through a series of numerical experiments, it is proved that the accuracy of the proposed neural network model for GFR estimation is higher than that of the model without BAS algorithm optimization. In addition, compared with the traditional one-step method, the method proposed in this article is more than 300% higher in the three performance indicators of MSE, MAE, and MAPE.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (with number 61906054), by Zhejiang Provincial Natural Science Foundation of China (with number LY21-F030006).

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Authors and Affiliations

  1. Department of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China

    Qing Wu, Zeyu Chen & Dechao Chen

  2. Department of Computing, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China

    Shuai Li

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  1. Qing Wu
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  2. Zeyu Chen
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Correspondence to Dechao Chen.

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Wu, Q., Chen, Z., Chen, D. et al. Beetle Antennae Search Strategy for Neural Network Model Optimization with Application to Glomerular Filtration Rate Estimation. Neural Process Lett 53, 1501–1522 (2021). https://doi.org/10.1007/s11063-021-10462-5

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