Молимо вас користите овај идентификатор за цитирање или овај линк до ове ставке: https://open.uns.ac.rs/handle/123456789/1143
Назив: A Machine Learning Approach for an Early Prediction of Preterm Delivery
Аутори: Despotovic D.
Zec A.
Mladenovic K.
Radin N.
Lončar-Turukalo, Tatjana 
Датум издавања: 5-нов-2018
Часопис: SISY 2018 - IEEE 16th International Symposium on Intelligent Systems and Informatics, Proceedings
Сажетак: © 2018 IEEE. The preterm birth presents a major cause of the infants' deaths, or the consequent health impairments globally, with an increasing trend of the preterm rate. The enormous global burden on both families and society calls for the preventive and predictive measures. The electrohysterogram (EHG), electrical activity of uterus as measured by surface electrodes, is a noninvasive and affordable tool for effective monitoring of both pregnancy and labour. In this study, the possibility of an early prediction of preterm delivery from the EHG recordings made between 22nd and 25th week of the gestation is explored. A set of novel features, including those exploiting signal's nonstationarity, based on the predictive modelling, and empirical mode decomposition, was evaluated on 15min long EHG recordings from the publicly available Term-Preterm EHG (TPEHG) database. On average, Random Forest classifier combined with artificial sampling, tested using 10-fold cross-validation on 322 samples (38 preterm) provided for 99.23% accuracy, with 98.40%sensitivity, and area under curve of 99%. The proposed approach has an additional advantage achieving the classification improvement over shorter, 15min long EHG recordings.
URI: https://open.uns.ac.rs/handle/123456789/1143
ISBN: 9781538668405
DOI: 10.1109/SISY.2018.8524818
Налази се у колекцијама:FTN Publikacije/Publications

Приказати целокупан запис ставки

SCOPUSTM   
Навођења

13
проверено 29.04.2023.

Преглед/и станица

45
Протекла недеља
8
Протекли месец
0
проверено 10.05.2024.

Google ScholarTM

Проверите

Алт метрика


Ставке на DSpace-у су заштићене ауторским правима, са свим правима задржаним, осим ако није другачије назначено.