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Optimized Feature Subset Selection Using Genetic Algorithm for Preterm Labor Prediction Based on Electrohysterography

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Optimized Feature Subset Selection Using Genetic Algorithm for Preterm Labor Prediction Based on Electrohysterography

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Nieto-Del-Amor, F.; Prats-Boluda, G.; Martínez-De-Juan, JL.; Díaz-Martínez, MDA.; Monfort-Ortiz, R.; Diago-Almela, VJ.; Ye Lin, Y. (2021). Optimized Feature Subset Selection Using Genetic Algorithm for Preterm Labor Prediction Based on Electrohysterography. Sensors. 21(10):1-15. https://doi.org/10.3390/s21103350

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/176381

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Title: Optimized Feature Subset Selection Using Genetic Algorithm for Preterm Labor Prediction Based on Electrohysterography
Author: Nieto-del-Amor, Félix Prats-Boluda, Gema Martínez-de-Juan, José L. Díaz-Martínez, María del Alba Monfort-Ortiz, Rogelio Diago-Almela, Vicente Jose Ye Lin, Yiyao
UPV Unit: Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica
Issued date:
Abstract:
[EN] Electrohysterography (EHG) has emerged as an alternative technique to predict preterm labor, which still remains a challenge for the scientific-technical community. Based on EHG parameters, complex classification ...[+]
Subjects: Preterm labor , Electrohysterography , Myoelectric activity , Genetic algorithm , Ensemble learning
Copyrigths: Reconocimiento (by)
Source:
Sensors. (eissn: 1424-8220 )
DOI: 10.3390/s21103350
Publisher:
MDPI AG
Publisher version: https://doi.org/10.3390/s21103350
Project ID:
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094449-A-I00/ES/ELECTROHISTEROGRAFIA PARA LA MEJORA EN LA TOMA DE DECISIONES EN SITUACIONES DE RIESGO EN OBSTETRICIA: PARTO PREMATURO E INDUCCION DEL PARTO/
info:eu-repo/grantAgreement/GENERALITAT VALENCIANA//AICO%2F2019%2F220//DESARROLLO DE HERRAMIENTAS DE USO CLINICO PARA LA PREDICCION DEL PARTO PREMATURO EN BASE A LA ELECTROHISTEROGRAFIA/
Thanks:
This work was supported by the Spanish Ministry of Economy and Competitiveness, the European Regional Development Fund (MCIU/AEI/FEDER, UE RTI2018-094449-A-I00-AR) and by the Generalitat Valenciana (AICO/2019/220).
Type: Artículo

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