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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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dc.contributor.author Nieto-del-Amor, Félix es_ES
dc.contributor.author Prats-Boluda, Gema es_ES
dc.contributor.author Martínez-de-Juan, José L. es_ES
dc.contributor.author Díaz-Martínez, María del Alba es_ES
dc.contributor.author Monfort-Ortiz, Rogelio es_ES
dc.contributor.author Diago-Almela, Vicente Jose es_ES
dc.contributor.author Ye Lin, Yiyao es_ES
dc.date.accessioned 2021-11-05T14:09:41Z
dc.date.available 2021-11-05T14:09:41Z
dc.date.issued 2021-05 es_ES
dc.identifier.uri http://hdl.handle.net/10251/176381
dc.description.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 algorithms involving non-linear transformation of the input features, which clinicians found difficult to interpret, were generally used to predict preterm labor. We proposed to use genetic algorithm to identify the optimum feature subset to predict preterm labor using simple classification algorithms. A total of 203 parameters from 326 multichannel EHG recordings and obstetric data were used as input features. We designed and validated 3 base classifiers based on k-nearest neighbors, linear discriminant analysis and logistic regression, achieving F1-score of 84.63 ± 2.76%, 89.34 ± 3.5% and 86.87 ± 4.53%, respectively, for incoming new data. The results reveal that temporal, spectral and non-linear EHG parameters computed in different bandwidths from multichannel recordings provide complementary information on preterm labor prediction. We also developed an ensemble classifier that not only outperformed base classifiers but also reduced their variability, achieving an F1-score of 92.04 ± 2.97%, which is comparable with those obtained using complex classifiers. Our results suggest the feasibility of developing a preterm labor prediction system with high generalization capacity using simple easy-to-interpret classification algorithms to assist in transferring the EHG technique to clinical practice. es_ES
dc.description.sponsorship 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). es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Sensors es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Preterm labor es_ES
dc.subject Electrohysterography es_ES
dc.subject Myoelectric activity es_ES
dc.subject Genetic algorithm es_ES
dc.subject Ensemble learning es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title Optimized Feature Subset Selection Using Genetic Algorithm for Preterm Labor Prediction Based on Electrohysterography es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/s21103350 es_ES
dc.relation.projectID 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/ es_ES
dc.relation.projectID 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/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica es_ES
dc.description.bibliographicCitation 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 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/s21103350 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 15 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 21 es_ES
dc.description.issue 10 es_ES
dc.identifier.eissn 1424-8220 es_ES
dc.identifier.pmid 34065847 es_ES
dc.identifier.pmcid PMC8151582 es_ES
dc.relation.pasarela S\437671 es_ES
dc.contributor.funder GENERALITAT VALENCIANA es_ES
dc.contributor.funder AGENCIA ESTATAL DE INVESTIGACION es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.subject.ods 03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades es_ES
upv.costeAPC 1173 es_ES


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