Foetal weight prediction based on improved PSO-GRNN model
by Fangxiong Chen; Guoheng Huang; Huishi Wu; Ke Hu; Weiwen Zhang; Lianglun Cheng
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 24, No. 2, 2020

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.

Online publication date: Wed, 07-Oct-2020

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Mining and Bioinformatics (IJDMB):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email