iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://unpaywall.org/10.1007/S12652-019-01230-4
Biomedical data analytics in mobile-health environments for high-risk pregnancy outcome prediction | Journal of Ambient Intelligence and Humanized Computing Skip to main content

Advertisement

Springer Nature Link
Log in

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

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Adomavicius G, Tuzhilin A (2015) Context-aware recommender systems. In: Ricci F (ed) Recommender systems handbook. Springer, Boston, pp 191–226

    Chapter  Google Scholar 

  • Alanazi HO, Abdullah AH, Qureshi KN (2017) A critical review for developing accurate and dynamic predictive models using machine learning methods in medicine and health care. J Med Syst 41(4):1–10

    Article  Google Scholar 

  • Alkema L, Chou D, Hogan D, Zhang S, Moller AB, Gemmill A, Fat DM, Boerma T, Temmerman M, Mathers C et al (2016) Global, regional, and national levels and trends in maternal mortality between 1990 and 2015, with scenario-based projections to 2030: a systematic analysis by the UN maternal mortality estimation inter-agency group. Lancet 387:462–474

    Article  Google Scholar 

  • Bantan MT (2016) Auto-segmentation of retinal blood vessels using image processing. In: The 4th saudi international conference on information technology, 6–9 Nov, Riyadh, Kingdom of Saudi Arabia. IEEE, pp 1–6

  • Bâra A, Lungu I (2012) Improving decision support systems with data mining techniques. In: Karahoca A (ed) Advances in data mining knowledge discovery and applications, chap 18. InTech, Rijeka

    Google Scholar 

  • Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit 30(7):1145–1159

    Article  Google Scholar 

  • Braga-Neto UM, Zollanvari A, Dougherty ER (2014) Cross-validation under separate sampling: strong bias and how to correct it. Bioinformatics 30(23):3349–3355

    Article  Google Scholar 

  • Ceccon S, Garway-Heath DF, Crabb DP, Tucker A (2014) Exploring early glaucoma and the visual field test: classification and clustering using bayesian networks. IEEE J Biomed Heal Inform 18(3):1008–1014

    Article  Google Scholar 

  • Chakraborty C, Gupta B, Ghosh SK (2016) Chronic wound characterization using bayesian classifier under telemedicine framework. Int J E-Health Med Commun 7(1):76–93

    Article  Google Scholar 

  • Chang C, Verhaegen PA, Duflou JR (2014) A comparison of classifiers for intelligent machine usage prediction. In: 2014 International conference on intelligent environments, 30 June —4 July, Shanghai, China. IEEE, pp 198–201

  • de Campos CP, Cuccu M, Corani G, Zaffalon M (2014) Extended tree augmented naive classifier. In: van der Gaag LC, Feelders AJ (eds) Probabilistic graphical models: 7th European workshop on probabilistic graphical models, 17–19 Sept, Utrecht, The Netherlands. Springer, pp 176–189

  • De Campos LM, Romero AE (2009) Bayesian network models for hierarchical text classification from a thesaurus. Int J Approx Reason 50(7):932–944

    Article  Google Scholar 

  • Deng X, Liu Q, Deng Y, Mahadevan S (2016) An improved method to construct basic probability assignment based on the confusion matrix for classification problem. Inf Sci (Ny) 340:250–261

    Article  Google Scholar 

  • Dieleman JL, Schneider MT, Haakenstad A, Singh L, Sadat N, Birger M, Reynolds A, Templin T, Hamavid H, Chapin A et al (2016) Development assistance for health: past trends, associations, and the future of international financial flows for health. Lancet 387:2536–2544

    Article  Google Scholar 

  • Duley L (2009) The global impact of pre-eclampsia and eclampsia. Semin Perinatol 33(3):130–137

    Article  Google Scholar 

  • Dunsmuir DT, Payne BA, Cloete G, Petersen CL, Görges M, Lim J, von Dadelszen P, Dumont GA, Ansermino JM (2014) Development of mhealth applications for pre-eclampsia triage. IEEE J Biomed Heal Inform 18(6):1857–1864

    Article  Google Scholar 

  • Fergus P, Selvaraj M, Chalmers C (2018) Machine learning ensemble modelling to classify caesarean section and vaginal delivery types using cardiotocography traces. Comput Biol Med 93:7–16

    Article  Google Scholar 

  • Ferradji MA, Zidani A (2016) Collaborative environment for remote clinical reasoning learning. Int J E-Health Med Commun 7(4):62–81

    Article  Google Scholar 

  • Ferrari RAP, Bertolozzi MR, Dalmas JC, Girotto E (2015) Gestational risks determinants of mothers of newborns in neonatal death. Acta Sci Heal Sci 37(1):41

    Article  Google Scholar 

  • Ghulmiyyah L, Sibai B (2012) Maternal mortality from preeclampsia/eclampsia. Semin Perinatol 36(1):56–59

    Article  Google Scholar 

  • Guo Y, Zhou L, He K, Gu Y, Sun Y (2014) Bayesian spam filtering mechanism based on decision tree of attribute set dependence in the MapReduce framework. Open Cybern Syst J 8:435–441

    Article  Google Scholar 

  • Hashi EK, Zaman MSU, Hasan MR (2017) An expert clinical decision support system to predict disease using classification techniques. In: International conference on electrical, computer and communication engineering (ECCE), 6–18 Feb, Cox’Bazar, Bangladesh. IEEE, pp 396–400

  • Karatzas K, Dourliou V, Kakaletsis N, Katsifarakis N, Savopoulos C, Hatzitolios AI (2017) A computational intelligence approach to diabetes mellitus and air quality levels in Thessaloniki, Greece. In: Wohlgemuth V, Fuchs-Kittowski F, Wittmann J (eds) Advances and new trends in environmental informatics: stability, continuity, innovation. Springer, Cham, pp 253–262

    Chapter  Google Scholar 

  • Lawn JE, Blencowe H, Waiswa P, Amouzou A, Mathers C, Hogan D, Flenady V, Frøen JF, Qureshi ZU, Calderwood C et al (2016) Stillbirths: rates, risk factors, and acceleration towards 2030. Lancet 387:587–603

    Article  Google Scholar 

  • Lei B, Li W, Yao Y, Jiang X, Tan EL, Qin J, Chen S, Ni D, Wang T (2017) Multi-modal and multi-layout discriminative learning for placental maturity staging. Pattern Recognit 63:719–730

    Article  Google Scholar 

  • Link WA, Sauer JR (2016) Bayesian cross-validation for model evaluation and selection, with application to the north american breeding bird survey. Ecology 97(7):1746–1758

    Google Scholar 

  • Magee LA, Pels A, Helewa M, Rey E, von Dadelszen P, Committee SHG et al (2014) Diagnosis, evaluation, and management of the hypertensive disorders of pregnancy: executive summary. J Obstet Gynaecol Can 36(7):575–576

    Article  Google Scholar 

  • Malasinghe LP, Ramzan N, Dahal K (2017) Remote patient monitoring: a comprehensive study. J Ambient Intell Human Comput 10(1):57–76

    Article  Google Scholar 

  • Manirabona A, Fourati LC, Boudjit S (2017) Investigation on healthcare monitoring systems: innovative services and applications. Int J E-Health Med Commun 8(1):1–18

    Article  Google Scholar 

  • Mertz L (2017) Turning the unknown into known: data mining is increasingly used to prospect for rare-disease biology and treatments. IEEE Pulse 8(1):28–32

    Article  Google Scholar 

  • Misirli AT, Bener AB (2014) Bayesian networks for evidence-based decision-making in software engineering. IEEE Trans Softw Eng 40(6):533–554

    Article  Google Scholar 

  • Mushcab H, Kernohan WG, Wallace J, Harper R, Martin S (2017) Self-management of diabetes mellitus with remote monitoring: a retrospective review of 214 cases. Int J E-Health Med Commun 8(1):52–61

    Article  Google Scholar 

  • Ni H, Cho S, Mankoff J, Yang J et al (2018) Automated recognition of hypertension through overnight continuous HRV monitoring. J Ambient Intell Hum Comput 9(6):2011–2023

    Article  Google Scholar 

  • Ohsaki M, Wang P, Matsuda K, Katagiri S, Watanabe H, Ralescu A (2017) Confusion-matrix-based kernel logistic regression for imbalanced data classification. IEEE Trans Knowl Data Eng 29(9):1086–1819

    Article  Google Scholar 

  • Orphanou K, Stassopoulou A, Keravnou E (2016) DBN-extended: a dynamic bayesian network model extended with temporal abstractions for coronary heart disease prognosis. IEEE J Biomed Heal Inform 20(3):944–952

    Article  Google Scholar 

  • Paydar K, Kalhori SRN, Akbarian M, Sheikhtaheri A (2017) A clinical decision support system for prediction of pregnancy outcome in pregnant women with systemic lupus erythematosus. Int J Med Inform 97:239–246

    Article  Google Scholar 

  • Penders J, Altini M, Van Hoof C, Dy E (2015) Wearable sensors for healthier pregnancies. Proc IEEE 103(2):179–191

    Article  Google Scholar 

  • Pereira S, Portela F, Santos MF, Machado J, Abelha A (2015) Predicting type of delivery by identification of obstetric risk factors through data mining. Procedia Comput Sci 64:601–609

    Article  Google Scholar 

  • Raffaeli L, Spinsante S, Gambi E (2016) Integrated smart tv-based personal e-health system. Int J E-Health Med Commun 7(1):48–64

    Article  Google Scholar 

  • Saxena K, Sharma R et al (2016) Efficient heart disease prediction system. Procedia Comput Sci 85:962–969

    Article  Google Scholar 

  • Tambe SB, Gajre SS (2018) Cluster-based real-time analysis of mobile healthcare application for prediction of physiological data. J Ambient Intell Hum Comput 9(2):429–445

    Article  Google Scholar 

  • Vehtari A, Gelman A, Gabry J (2016) Practical bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat Comput 27(5):1413–1432

    Article  MathSciNet  MATH  Google Scholar 

  • Veloso R, Portela F, Santos MF, Machado J, da Silva Abelha A, Rua F, Silva Á (2017) Categorize readmitted patients in intensive medicine by means of clustering data mining. Int J E-Health Med Commun 8(3):22–37

    Article  Google Scholar 

  • Verma L, Srivastava S, Negi P (2016) A hybrid data mining model to predict coronary artery disease cases using non-invasive clinical data. J Med Syst 40(7):178

    Article  Google Scholar 

  • Vest AR, Cho LS (2014) Hypertension in pregnancy. Curr Atheroscler Rep 16(3):1–11

    Article  Google Scholar 

  • Wang L, Xie Y, Zhou H, Wang Y, Guo J (2016) Learning based k-dependence bayesian classifiers. In: International conference on cloud computing and security, 29–31 July, Nanjing, China. Springer, pp 553–566

  • Zuckerwar AJ, Pretlow RA, Stoughton JW, Baker DA (1993) Development of a piezopolymer pressure sensor for a portable fetal heart rate monitor. IEEE Trans Biomed Eng 40(9):963–969

    Article  Google Scholar 

Download references

Acknowledgements

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.

Author information

Authors and Affiliations

  1. Instituto de Telecomunicações, Universidade da Beira Interior, Covilhã, Portugal

    Mário W. L. Moreira & Joel J. P. C. Rodrigues

  2. Instituto Federal de Educação, Ciência e Tecnologia do Ceará (IFCE), Aracati, CE, Brazil

    Mário W. L. Moreira

  3. National Institute of Telecommunications (Inatel), Santa Rita do Sapucaí, MG, Brazil

    Joel J. P. C. Rodrigues

  4. College of Computer and Information Sciences (CCIS), King Saud University, Riyadh, 12372, Saudi Arabia

    Joel J. P. C. Rodrigues & Jalal Al-Muhtadi

  5. Universidade Federal do Ceará (UFC), Fortaleza, CE, Brazil

    Francisco H. C. Carvalho

  6. Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, VIC, 3086, Australia

    Naveen Chilamkurti

  7. ITMO University, Saint Petersburg, Russia

    Joel J. P. C. Rodrigues & Victor Denisov

Authors
  1. Mário W. L. Moreira
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joel J. P. C. Rodrigues
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francisco H. C. Carvalho
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Naveen Chilamkurti
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jalal Al-Muhtadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Victor Denisov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Joel J. P. C. Rodrigues.

Ethics declarations

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.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Moreira, M.W.L., Rodrigues, J.J.P.C., Carvalho, F.H.C. et al. Biomedical data analytics in mobile-health environments for high-risk pregnancy outcome prediction. J Ambient Intell Human Comput 10, 4121–4134 (2019). https://doi.org/10.1007/s12652-019-01230-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-019-01230-4

Keywords

Search

Navigation