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11 - Computer Science and Informatics

Liverpool John Moores University

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Article title

Prediction of Preterm Deliveries from EHG Signals Using Machine Learning

Type

D - Journal article

DOI

10.1371/journal.pone.0077154

Title of journal

PLoS ONE

Article number

Volume number

Issue number

First page of article

e77154

ISSN of journal

1932-6203

Year of publication

2013

URL

Number of additional authors

Additional information

<24> This work discussed the very important live saving problem of predicting preterm deliveries for pregnant women from the EHG signals using machine learning algorithms. We have proposed the use of a polynomial classifier for the classification of term and preterm EHG signals in which our approach showed significant improvement on existing studies with 96% sensitivity and 90% specificity.

Interdisciplinary

Cross-referral requested

Research group

None

Citation count

Proposed double-weighted

Double-weighted statement

Reserve for a double-weighted output

Non-English

English abstract