Nature-Inspired Algorithm for Training Multilayer Perceptron Networks in e-health Environments for High-Risk Pregnancy Care

  • Mário W. L. Moreira
  • Joel J. P. C. Rodrigues
  • Neeraj Kumar
  • Jalal Al-Muhtadi
  • Valery Korotaev
Part of the following topical collections:
  1. Convergence of Deep Machine Learning and Nature Inspired Computing Paradigms for Medical Informatics


Nature presents an infinite source of inspiration for computational models and paradigms, in particular for researchers associated with the area known as natural computing. The simultaneous optimization of the architectures and weights of artificial neural networks (ANNs) through biologically inspired algorithms is an interesting approach for obtaining efficient networks with relatively good generalization capabilities. This methodology constitutes a concordance between a low structural complexity model and low training error rates. Currently, complexity and high error rates are the leading issues faced in the development of clinical decision support systems (CDSSs) for pregnancy care. Hence, in this paper the use of a biologically inspired technique, known as particle swarm optimization (PSO), is proposed for reducing the computational cost of the ANN-based method referred to as the multilayer perceptron (MLP), without reducing its precision rate. The results show that the PSO algorithm is able to improve computational model performance, showing lower validation error rates than the conventional approach. This technique can select the best parameters and provide an efficient solution for training the MLP algorithm. The proposed nature-inspired algorithm and its parameter adjustment method improve the performance and precision of CDSSs. This technique can be applied in electronic health (e-health) systems as a useful tool for handling uncertainty in the decision-making process related to high-risk pregnancy. The proposed method outperformed, on average, other approaches by 26.4% in terms of precision and 14.9% in terms of the true positive ratio (TPR), and showed a reduction of 35.4% in the false positive ratio (FPR). Furthermore, this method was superior to the MLP algorithm in terms of precision and area under the receiver operating characteristic curve by 2.3 and 10.2%, respectively, when applied to the delivery outcome for pregnant women.


Clinical decision support systems Nature inspired computing Machine learning Artificial neural networks Optimization e-Health 



This work was supported by the Research Center of College of Computer and Information Sciences, King Saud University, by the National Funding from the FCT - Fundação para a Ciência e a Tecnologia through the UID/EEA/50008/2013 Project, by the Government of Russian Federation, Grant 074-U01, 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, and by Ciência sem Fronteiras of CNPq, Brazil, through the process number 207706/ 2014-0. The authors are grateful for this support.

Compliance with Ethical Standards

Conflict of interests

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 Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Instituto de TelecomunicaçõesUniversidade da Beira Interior (UBI)CovilhãPortugal
  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.University of Fortaleza (UNIFOR)FortalezaBrazil
  5. 5.ITMO UniversitySaint PetersburgRussia
  6. 6.Department of Computer Science and EngineeringThapar UniversityPatiala (Punjab)India
  7. 7.College of Computer and Information Sciences (CCIS)King Saud UniversityRiyadhSaudi Arabia

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