Abstract
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.
References
Siddique N., Adeli H.: Brief history of natural sciences for nature-inspired computing in engineering. J. Civ. Eng. Manag. 22 (3): 287–301, 2016. https://doi.org/10.3846/13923730.2016.1157095
D’Addona D. M., Ullah A. S., Matarazzo D.: Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing. J. Intell. Manuf. 28 (6): 1285–1301, 2017. https://doi.org/10.1007/s10845-015-1155-0
Nanda S. J., Panda G.: A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm. Evol. Comput. 16: 1–18, 2014. https://doi.org/10.1016/j.swevo.2013.11.003
Dorigo M.: Ten years of swarm intelligence. Swarm. Intell. 10 (4): 245–246, 2016. https://doi.org/10.1007/s11721-016-0130-5
Yang X. S.: Swarm intelligence based algorithms: a critical analysis. Evol. Intell. 7 (1): 17–28, 2014. https://doi.org/10.1007/s12065-013-0102-2
Liao T., Socha K., de Oca M. A. M., Stützle T., Dorigo M.: Ant colony optimization for mixed-variable optimization problems. IEEE Trans. Evol. Comput. 18 (4): 503–518, 2014. https://doi.org/10.1109/TEVC.2013.2281531
Cleghorn C. W., Engelbrecht A. P.: A generalized theoretical deterministic particle swarm model. Swarm Intell. 8 (1): 35–59, 2014. https://doi.org/10.1007/s11721-013-0090-y
Zhang W., Yen G. G., He Z.: Constrained optimization via artificial immune system. IEEE Trans. Cybern. 44 (2): 185–198, 2014. https://doi.org/10.1109/TCYB.2013.2250956
Tsai C. F., Eberle W., Chu C. Y.: Genetic algorithms in feature and instance selection. Knowl.-Based Syst. 39: 240–247, 2013. https://doi.org/10.1016/j.knosys.2012.11.005
Si L., Wang Z., Liu Z., Liu X., Tan C., Xu R.: Health condition evaluation for a shearer through the integration of a fuzzy neural network and improved particle swarm optimization algorithm. Appl. Sci. 6 (6): 171, 2016. https://doi.org/10.3390/app6060171
Zhang L., Zheng Y., Wang K., Zhang X., Zheng Y.: An optimized nash nonlinear grey Bernoulli model based on particle swarm optimization and its application in prediction for the incidence of Hepatitis B in Xinjiang, China. Comput. Biol. Med. 49: 67–73, 2014. https://doi.org/10.1016/j.compbiomed.2014.02.008
Martin S., Choi C. T.: Nonlinear electrical impedance tomography reconstruction using artificial neural networks and particle swarm optimization. IEEE Trans. Magn. 52 (3): 1–4, 2016. https://doi.org/10.1109/TMAG.2015.2488901
Wang P., Lin J., Wang M.: An image reconstruction algorithm for electrical capacitance tomography based on simulated annealing particle swarm optimization. J. Appl. Res. Technol. 13 (2): 197–204, 2015. https://doi.org/10.1016/j.jart.2015.06.018
Liao X., Yin J., Guo S., Li X., Sangaiah A. K. Medical JPEG image steganography based on preserving inter-block dependencies. Comput. Electr. Eng., 2017 https://doi.org/10.1016/j.compeleceng.2017.08.020
Zhang R., Shen J., Wei F., Li X., Sangaiah A. K.: Medical image classification based on multi-scale non-negative sparse coding. Artif. Intell. Med. 83: 44–51, 2017. https://doi.org/10.1016/j.artmed.2017.05.006
Takkar S., Singh A., Pandey B.: Application of machine learning algorithms to a well defined clinical problem: liver disease. Int. J. E-Health Med. Commun. 8 (4): 38–60, 2017. https://doi.org/10.4018/IJEHMC.2017100103
Gaxiola F., Melin P., Valdez F., Castillo O.: Interval type-2 fuzzy weight adjustment for backpropagation neural networks with application in time series prediction. Inf. Sci. (Ny) 260: 1–14, 2014. https://doi.org/10.1016/j.ins.2013.11.006
Hlihor R. M., Diaconu M., Leon F., Curteanu S., Tavares T., Gavrilescu M.: Experimental analysis and mathematical prediction of cd(II) removal by biosorption using support vector machines and genetic algorithms. N. Biotechnol. 32 (3): 358–368, 2015. https://doi.org/10.1016/j.nbt.2014.08.003
Chen F., Tang B., Song T., Li L.: Multi-fault diagnosis study on roller bearing based on multi-kernel support vector machine with chaotic particle swarm optimization. Measurement 47: 576–590, 2014. https://doi.org/10.1016/j.measurement.2013.08.021
Asadi E., da Silva M. G., Antunes C. H., Dias L., Glicksman L.: Multi-objective optimization for building retrofit: a model using genetic algorithm and artificial neural network and an application. Energy Build. 81: 444–456, 2014. https://doi.org/10.1016/j.enbuild.2014.06.009
Cheng Y. C., Hartmann T., Tsai P. Y., Middendorf M.: Population based ant colony optimization for reconstructing ECG signals. Evol. Intell. 9 (3): 55–66, 2016. https://doi.org/10.1007/s12065-016-0139-0
Veloso R., Portela F., Santos M. F., Machado J., da Silva Abelha A., Rua F., Silva Á.: Categorize readmitted patients in intensive medicine by means of clustering data mining. Int. J. E-Health Med. Commun. 8 (3): 22–37, 2017. https://doi.org/10.4018/IJEHMC.2017070102
Hedeshi N. G., Abadeh M. S.: Coronary artery disease detection using a fuzzy-boosting PSO approach. Comput. Intell. Neurosci. 2014: 6, 2014. https://doi.org/10.1155/2014/783734
Samuel O. W., Asogbon G. M., Sangaiah A. K., Fang P., Li G.: An integrated decision support system based on ANN and fuzzy_AHP for heart failure risk prediction. Expert. Syst. Appl. 68: 163–172, 2017. https://doi.org/10.1016/j.eswa.2016.10.020
Liang C., Peng L.: An automated diagnosis system of liver disease using artificial immune and genetic algorithms. J. Med. Syst. 37 (2): 9932, 2013. https://doi.org/10.1007/s10916-013-9932-9
Chernbumroong S., Cang S., Yu H.: Genetic algorithm-based classifiers fusion for multisensor activity recognition of elderly people. IEEE J. Biomed. Heal. Informatics 19 (1): 282–289, 2015. https://doi.org/10.1109/JBHI.2014.2313473
Vishnuvarthanan A., Rajasekaran M. P., Govindaraj V., Zhang Y., Thiyagarajan A.: An automated hybrid approach using clustering and nature inspired optimization technique for improved tumor and tissue segmentation in magnetic resonance brain images. Appl. Soft. Comput. 57: 399–426, 2017. https://doi.org/10.1016/j.asoc.2017.04.023
Liang W., Tang M., Jing L., Sangaiah A. K., Huang Y. SIRSE: a secure identity recognition scheme based on electroencephalogram data with multi-factor feature. Comput. Electr. Eng., 2017. https://doi.org/10.1016/j.compeleceng.2017.05.001
Zhou L.: Qoe-driven delay announcement for cloud mobile media. IEEE Trans. Circuits. Syst. Video Technol. 27 (1): 84–94, 2017. https://doi.org/10.1109/TCSVT.2016.2539698
Zhou L.: Mobile device-to-device video distribution: theory and application. ACM Trans. Multimed. Comput. Commun. 12 (3): 38, 2016. https://doi.org/10.1145/2886776
Hassanien A. E., Moftah H. M., Azar A. T., Shoman M.: MRI breast cancer diagnosis hybrid approach using adaptive ant-based segmentation and multilayer perceptron neural networks classifier. Appl. Soft. Comput. 14: 62–71, 2014. https://doi.org/10.1016/j.asoc.2013.08.011
Tang J., Deng C., Huang G. B.: Extreme learning machine for multilayer perceptron. IEEE Trans. Neural Networks Learn. Syst. 27 (4): 809–821, 2016. https://doi.org/10.1109/TNNLS.2015.2424995
Zhang Y., Sun Y., Phillips P., Liu G., Zhou X., Wang S.: A multilayer perceptron based smart pathological brain detection system by fractional fourier entropy. J. Med. Syst. 40 (7): 173, 2016. https://doi.org/10.1007/s10916-016-0525-2
Samuel O. W., Zhou H., Li X., Wang H., Zhang H., Sangaiah A. K., Li G. Pattern recognition of electromyography signals based on novel time domain features for amputees’ limb motion classification. Comput. Electr. Eng., 2017. https://doi.org/10.1016/j.compeleceng.2017.04.003
Krstajic D., Buturovic L. J., Leahy D. E., Thomas S.: Cross-validation pitfalls when selecting and assessing regression and classification models. J. Cheminform. 6 (1): 10, 2014. https://doi.org/10.1186/1758-2946-6-10
Ma H., Bandos A. I., Rockette H. E., Gur D.: On use of partial area under the roc curve for evaluation of diagnostic performance. Stat. Med. 32 (20): 3449–3458, 2013. https://doi.org/10.1002/sim.5777
Pereira S., Portela F., Santos M. F., Machado J., Abelha A.: Predicting type of delivery by identification of obstetric risk factors through data mining. Procedia. Comput. Sci. 64: 601–609, 2015. https://doi.org/10.1016/j.procs.2015.08.573
Paydar K., Kalhori S. R. N., Akbarian M., Sheikhtaheri A.: A clinical decision support system for prediction of pregnancy outcome in pregnant women with systemic lupus erythematosus. Int. J. Med. Inform. 97: 239–246, 2017. https://doi.org/10.1016/j.ijmedinf.2016.10.018
Acknowledgements
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.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
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.
Additional information
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.
This article is part of the Topical Collection on Patient Facing Systems
Rights and permissions
About this article
Cite this article
Moreira, M.W.L., Rodrigues, J.J.P.C., Kumar, N. et al. Nature-Inspired Algorithm for Training Multilayer Perceptron Networks in e-health Environments for High-Risk Pregnancy Care. J Med Syst 42, 51 (2018). https://doi.org/10.1007/s10916-017-0887-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10916-017-0887-0