Fetal Birth Weight Estimation in High-Risk Pregnancies Through Machine Learning Techniques

Mario W.L. Moreira, Joel J.P.C. Rodrigues, Vasco Furtado, Constandinos X. Mavromoustakis, Neeraj Kumar, Isaac Woungang

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    The low weight of fetus at birth is considered one of the most critical problems in pregnancy care, affecting the newborn's health and leading it to death in more severe cases. This condition is responsible for the high infant mortality rates worldwide. In health, artificial intelligence techniques, especially those based on machine learning (ML), can early predict problems related to the fetus' health state during entire gestation, including at birth. Hence, this paper proposes an analysis of several ML techniques capable of predicting whether the fetus will born small for its gestational age. The results show that the hybrid model, named bagged tree, achieved excellent results concerning accuracy and area under the receiver operating characteristic curve, to know, 0.849 and 0.636, respectively. The importance of the early diagnosis of problems related to fetal development relies on the possibility of an increase in the gestation days through timely intervention. Such intervention would allow an improvement in fetal weight at birth, associated with a decrease in neonatal morbidity and mortality.

    Original languageEnglish
    Title of host publication2019 IEEE International Conference on Communications, ICC 2019 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781538680889
    DOIs
    Publication statusPublished - 1 May 2019
    Event2019 IEEE International Conference on Communications, ICC 2019 - Shanghai, China
    Duration: 20 May 201924 May 2019

    Publication series

    NameIEEE International Conference on Communications
    Volume2019-May
    ISSN (Print)1550-3607

    Conference

    Conference2019 IEEE International Conference on Communications, ICC 2019
    CountryChina
    CityShanghai
    Period20/05/1924/05/19

    Fingerprint

    Learning systems
    Health
    Artificial intelligence

    Cite this

    Moreira, M. W. L., Rodrigues, J. J. P. C., Furtado, V., Mavromoustakis, C. X., Kumar, N., & Woungang, I. (2019). Fetal Birth Weight Estimation in High-Risk Pregnancies Through Machine Learning Techniques. In 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings [8761985] (IEEE International Conference on Communications; Vol. 2019-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICC.2019.8761985
    Moreira, Mario W.L. ; Rodrigues, Joel J.P.C. ; Furtado, Vasco ; Mavromoustakis, Constandinos X. ; Kumar, Neeraj ; Woungang, Isaac. / Fetal Birth Weight Estimation in High-Risk Pregnancies Through Machine Learning Techniques. 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. (IEEE International Conference on Communications).
    @inproceedings{6386410158bb40538d987b9c4173faad,
    title = "Fetal Birth Weight Estimation in High-Risk Pregnancies Through Machine Learning Techniques",
    abstract = "The low weight of fetus at birth is considered one of the most critical problems in pregnancy care, affecting the newborn's health and leading it to death in more severe cases. This condition is responsible for the high infant mortality rates worldwide. In health, artificial intelligence techniques, especially those based on machine learning (ML), can early predict problems related to the fetus' health state during entire gestation, including at birth. Hence, this paper proposes an analysis of several ML techniques capable of predicting whether the fetus will born small for its gestational age. The results show that the hybrid model, named bagged tree, achieved excellent results concerning accuracy and area under the receiver operating characteristic curve, to know, 0.849 and 0.636, respectively. The importance of the early diagnosis of problems related to fetal development relies on the possibility of an increase in the gestation days through timely intervention. Such intervention would allow an improvement in fetal weight at birth, associated with a decrease in neonatal morbidity and mortality.",
    author = "Moreira, {Mario W.L.} and Rodrigues, {Joel J.P.C.} and Vasco Furtado and Mavromoustakis, {Constandinos X.} and Neeraj Kumar and Isaac Woungang",
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    Moreira, MWL, Rodrigues, JJPC, Furtado, V, Mavromoustakis, CX, Kumar, N & Woungang, I 2019, Fetal Birth Weight Estimation in High-Risk Pregnancies Through Machine Learning Techniques. in 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings., 8761985, IEEE International Conference on Communications, vol. 2019-May, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE International Conference on Communications, ICC 2019, Shanghai, China, 20/05/19. https://doi.org/10.1109/ICC.2019.8761985

    Fetal Birth Weight Estimation in High-Risk Pregnancies Through Machine Learning Techniques. / Moreira, Mario W.L.; Rodrigues, Joel J.P.C.; Furtado, Vasco; Mavromoustakis, Constandinos X.; Kumar, Neeraj; Woungang, Isaac.

    2019 IEEE International Conference on Communications, ICC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 8761985 (IEEE International Conference on Communications; Vol. 2019-May).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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    AU - Moreira, Mario W.L.

    AU - Rodrigues, Joel J.P.C.

    AU - Furtado, Vasco

    AU - Mavromoustakis, Constandinos X.

    AU - Kumar, Neeraj

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    AB - The low weight of fetus at birth is considered one of the most critical problems in pregnancy care, affecting the newborn's health and leading it to death in more severe cases. This condition is responsible for the high infant mortality rates worldwide. In health, artificial intelligence techniques, especially those based on machine learning (ML), can early predict problems related to the fetus' health state during entire gestation, including at birth. Hence, this paper proposes an analysis of several ML techniques capable of predicting whether the fetus will born small for its gestational age. The results show that the hybrid model, named bagged tree, achieved excellent results concerning accuracy and area under the receiver operating characteristic curve, to know, 0.849 and 0.636, respectively. The importance of the early diagnosis of problems related to fetal development relies on the possibility of an increase in the gestation days through timely intervention. Such intervention would allow an improvement in fetal weight at birth, associated with a decrease in neonatal morbidity and mortality.

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    Moreira MWL, Rodrigues JJPC, Furtado V, Mavromoustakis CX, Kumar N, Woungang I. Fetal Birth Weight Estimation in High-Risk Pregnancies Through Machine Learning Techniques. In 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. 8761985. (IEEE International Conference on Communications). https://doi.org/10.1109/ICC.2019.8761985