Title: Foetal weight prediction based on improved PSO-GRNN model

Authors: Fangxiong Chen; Guoheng Huang; Huishi Wu; Ke Hu; Weiwen Zhang; Lianglun Cheng

Addresses: School of Automation, Guangdong University of Technology, Guangzhou, Guangdong, China ' School of Computers, Guangdong University of Technology, Guangzhou, Guangdong, China ' School of Computers, Guangdong University of Technology, Guangzhou, Guangdong, China ' School of Computers, Guangdong University of Technology, Guangzhou, Guangdong, China ' School of Computers, Guangdong University of Technology, Guangzhou, Guangdong, China ' School of Computers, Guangdong University of Technology, Guangzhou, Guangdong, China

Abstract: Foetal weight prediction is important for foetal development and safety of pregnant women. However, foetal weight can only be roughly predicted using the ultrasound data set of pregnant women, and the prediction accuracy is still low. In this paper, we propose a prediction model, termed PSO-GRNN, which is based on Particle Swarm Optimisation algorithm and Generalised Regression Neural Network, in order to obtain the foetal weight using the physical examination data and ultrasonic data of pregnant women. The historical data of pregnant women's examination are pre-processed firstly, and a prediction model is established by GRNN and then the parameters of the prediction model are optimised to reduce human interference by using improved particle swarm optimisation algorithm. The experimental results show that on average compared with some state-of-the-art algorithms, the Mean Relative Error of the proposed method is 1.33% lower and the accuracy of foetal weight prediction is 4.15% higher respectively.

Keywords: foetal weight prediction; pregnant women prenatal; feature normalisation; particle swarm optimisation; GRNN; generalised regression neural network; regression model; deep learning; ultrasound.

DOI: 10.1504/IJDMB.2020.110161

International Journal of Data Mining and Bioinformatics, 2020 Vol.24 No.2, pp.177 - 200

Received: 06 Apr 2020
Accepted: 14 Jul 2020
Published online: 07 Oct 2020 *

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