Biomedical data analytics in mobile-health environments for high-risk pregnancy outcome prediction

  • Mário W. L. Moreira
  • Joel J. P. C. RodriguesEmail author
  • Francisco H. C. Carvalho
  • Naveen Chilamkurti
  • Jalal Al-Muhtadi
  • Victor Denisov
Original Research


According to the World Health Organization (WHO), a significant reduction in mortality and maternal morbidity has occurred in developed countries over the past decades. In contrast, these rates remain high in developing countries. Smart mobile-health (m-health) applications that use machine learning (ML) approaches are necessary tools for pregnancy monitoring in an accessible, reliable, and cost-efficient manner, making the prediction of high-risk situations possible during gestation. This paper, therefore, proposes the development, performance evaluation, and comparison of ML algorithms based on Bayesian networks capable of identifying at-risk pregnancies based on the symptoms and risk factors presented by the patients. A performance comparison of several Bayes-based ML algorithms determined the best-suited algorithm for the prediction, identification, and accompaniment of hypertensive disorders during pregnancy. The contribution of this study focuses on finding a smart classifier for the development of novel mobile devices, which presents reliable results in the identification of problems related to pregnancy. Through the well-known cross-validation method, this proposal is evaluated and compared with other recent approaches. The averaged one-dependence estimators presented better results on average than the other approaches. These findings are key to improving the health monitoring of women suffering from high-risk pregnancies around the world. Thus, this study can contribute to a reduction of both maternal and fetal deaths.


Mobile health Data analytics Machine learning Bayesian inference Hypertensive disorders Pregnancy 



This work was supported by the National Funding from the FCT—Fundação para a Ciência e a Tecnologia through the UID/EEA/50008/2019 Project; by the Government of Russian Federation, Grant 08-08; by Finep, with resources from Funttel, Grant no. 01.14.0231.00, under the Centro de Referência em Radiocomunicações—CRR project of the Instituto Nacional de Telecomunicações (Inatel), Brazil; by Brazilian National Council for Research and Development (CNPq) via Grant no. 309335/2017-5; by Ciência sem Fronteiras of CNPq, Brazil, through the process number 207706/2014-0; and by the Research Center of the College of Computer and Information Sciences, King Saud University. The authors are grateful for this support.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

The ethics board approval was obtained by the Research Ethics Committee of the Maternity School Assis Chateaubriand of the Federal University of Ceará, Fortaleza, CE, Brazil under the certificate of presentation for ethical appreciation, number 66929317.0.0000.5050, and receiving assent with protocol number 2.036.062.

Informed consent

Informed consent was obtained from all individual participants included in the study.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Instituto de TelecomunicaçõesUniversidade da Beira InteriorCovilhãPortugal
  2. 2.Instituto Federal de EducaçãoCiência e Tecnologia do Ceará (IFCE)AracatiBrazil
  3. 3.National Institute of Telecommunications (Inatel)Santa Rita do SapucaíBrazil
  4. 4.College of Computer and Information Sciences (CCIS)King Saud UniversityRiyadhSaudi Arabia
  5. 5.Universidade Federal do Ceará (UFC)FortalezaBrazil
  6. 6.Department of Computer Science and Computer EngineeringLa Trobe UniversityMelbourneAustralia
  7. 7.ITMO UniversitySaint PetersburgRussia

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