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Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women

Hussain, A, Fergus, P, Al-Askar, H, Al-Jumeily, D and Jager, F (2015) Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women. NEUROCOMPUTING, 151. pp. 963-974. ISSN 0925-2312

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There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants is most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. There is a strong body of evidence emerging that suggests the analysis of uterine electrical signals, from the abdominal surface (Electrohysterography – EHG), could provide a viable way of diagnosing true labour and even predict preterm deliveries. This paper explores this idea further and presents a new dynamic self-organized network immune algorithm that classifies term and preterm records, using an open dataset containing 300 records (38 preterm and 262 term). Using the dataset, oversampling and cross validation techniques are evaluated against other similar studies. The proposed approach shows an improvement on existing studies with 89% sensitivity, 91% specificity, 90% positive predicted value, 90% negative predicted value, and an overall accuracy of 90%.

Item Type: Article
Additional Information: Embargo requested: Not known
Uncontrolled Keywords: 08 Information And Computing Sciences, 09 Engineering, 17 Psychology And Cognitive Sciences
Subjects: B Philosophy. Psychology. Religion > BF Psychology
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RG Gynecology and obstetrics
Divisions: Computer Science & Mathematics
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Date Deposited: 29 Apr 2016 10:11
Last Modified: 18 May 2022 09:39
DOI or ID number: 10.1016/j.neucom.2014.03.087
URI: https://researchonline.ljmu.ac.uk/id/eprint/2369
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