Abstract
Hypertension is becoming a global epidemic for the developing world and continuous blood pressure monitoring and early diagnosis is vital for the prevention of this disease. However, in real life, patients are usually unable to maintain frequent monitoring because of reasons that include forgetfulness, human error and/or machine error. This paper presents a personalized prediction model for blood pressure using Recurrent Kernel Extreme Reservoir Machine (RKERM). This technique combines reservoir computing with RKELM to perform multistep ahead prediction. We use RKERM for blood pressure prediction and its performance is evaluated with other ELM based prediction algorithms. To evaluate our model, we use real world blood pressure data collected from Malaysian population consisting of hypertensive and non-hypertensive patients. The experimental results show that the proposed prediction mechanism has higher prediction accuracy than existing ELM methods.
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References
Rosendorff, C.: Essential Cardiology: Principles and Practice. Springer, Heidelberg (2005)
Omar, M.A., Irfan, N.I., Yil, K.Y., Muksan, N., Abdul Majid, N.L., Mohd Yusoff, M.F.: Prevalence of young adult hypertension in Malaysia and its associated factors: findings from national health and morbidity survey 2011. Malays. J. Public Health Med. 16(3), 274–283 (2016)
Ehret, G.B., Munroe, P.B., Rice, K.M., Bochud, M., Johnson, A.D., Chasman, D.I., Smith, A.V., Tobin, M.D., Verwoert, G.C., Hwang, S.J., et al.: Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 478(7367), 103–109 (2011)
Marik, P.E., Varon, J.: Hypertensive crises: challenges and management. Chest 131(6), 1949–1962 (2007)
Kearney, P.M., Whelton, M., Reynolds, K., Muntner, P., Whelton, P.K., He, J.: Global burden of hypertension: analysis of worldwide data. The Lancet 365(9455), 217–223 (2005)
Institute for Public Health: National Health and Morbidity Survey 2011 (NHMS 2011): Non-Communicable Disease (2011)
Institute for Public Health: The Third National Health and Morbidity Survey (NHMS III) (2006), (2008)
Krishnan, A., Garg, R., Kahandaliyanage, A.: Hypertension in the South-East Asia region: an overview. In: World Health Organization South East Asia Region Regional Health Forum, vol. 17, no. 1 (2013)
World Health Organization: A global brief on Hypertension, World Health Day (2013)
Mayo Clinic: Get the most out of home blood pressure monitoring, 07 March 2018. https://www.mayoclinic.org/diseases-conditions/high-blood-pressure/in-depth/high-blood-pressure/art-20047889. Accessed 16 July 2018
Amato, F., López, A., Peña-Méndez, E.M., Vaňhara, P., Hampl, A., Havel, J.: Artificial neural networks in medical diagnosis. J. Appl. Biomed. 11(2), 47–58 (2013)
CireÅŸan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. In: International Conference on Medical Image Computing and Computer-assisted Intervention (2013)
Samant, R., Rao, S.: Evaluation of artificial neural networks in prediction of essential hypertension. Int. J. Comput. Appl. 81(12), 34–38 (2013)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)
Ding, S., Xu, X., Nie, R.: Extreme learning machine and its applications. Neural Comput. Appl. 25(3–4), 549–556 (2014)
Liu, N., Cao, J., Koh, Z.X., Pek, P.P., Ong, M.E.H.: Risk stratification with extreme learning machine: a retrospective study on emergency department patients. Math. Probl. Eng. 2014, 6 (2014)
Liang, N.Y., Saratchandran, P., Huang, G.B., Sundararajan, N.: Classification of mental tasks from EEG signals using extreme learning machine. Int. J. Neural Syst. 16(1), 29–38 (2006)
Song, Y., Crowcroft, J., Zhang, J.: Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine. J. Neurosci. Meth. 210(2), 132–146 (2012)
Chen, F.L., Ou, T.Y.: Sales forecasting system based on gray extreme learning machine with Taguchi method in retail industry. Expert Syst. Appl. 38(3), 1336–1345 (2011)
Zhu, C., Yin, J., Li, Q.: A stock decision support system based on ELM. In: Extreme Learning Machines 2013: Algorithms and Applications, pp. 67–79 (2014)
Liu, Z., Loo, C.K., Masuyama, N., Pasupa, K.: Recurrent kernel extreme reservoir machine for time series prediction. IEEE Access 6, 19583–19596 (2018)
Al-Shayea, Q.K.: Artificial neural networks in medical diagnosis. Int. J. Comput. Sci. Issues 8(2), 150–154 (2011)
Takeda, T., Nakajima, H., Tsuchiya, N., Hata, Y.: A fuzzy human model for blood pressure estimation. In: Advanced Intelligent Systems, pp. 109–124 (2014)
Li, X., Wu, S., Wang, L.: Blood pressure prediction via recurrent models with contextual layer. In: 26th International Conference on World Wide Web (2017)
Ghosh, S., Banerjee, A., Ray, N., Wood, P.W., Boulanger, P., Padwal, R.: Continuous blood pressure prediction from pulse transit time using ECG and PPG signals. In: Healthcare Innovation Point-Of-Care Technologies Conference (HI-POCT) (2016)
Golino, H.F., Amaral, L.S.D.B., Duarte, S.F.P., Gomes, C.M.A., Soares, T.D.J., Reis, L.A.D., Santos, J.: Predicting increased blood pressure using machine learning. J. Obes. 2014, 12 (2014)
Wu, T.H., Pang, G.K., Kwong, E.W.: Predicting systolic blood pressure using machine learning. In: 7th International Conference on Information and Automation for Sustainability, Colombo (2014)
LaFreniere, D., Zulkernine, F., Barber, D., Martin, K.: Using machine learning to predict hypertension from a clinical dataset. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI) (2016)
Kwong, E.W.Y., Wu, H., Pang, G.K.H.: A prediction model of blood pressure for telemedicine. Health Inform. J. (2016). https://doi.org/10.1177/1460458216663025
Chorowski, J., Wang, J., Zurada, J.M.: Review and performance comparison of SVM-and ELM-based classifiers. Neurocomputing 128, 507–516 (2014)
Donders, A.R.T., Van Der Heijden, G.J., Stijnen, T., Moons, K.G.: A gentle introduction to imputation of missing values. J. Clin. Epidemiol. 59(10), 1087–1091 (2006)
Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data. Wiley, Hoboken (2014)
Park, J.M., Kim, J.H.: Online recurrent extreme learning machine and its application to time-series prediction. In: 2017 International Joint Conference on Neural Networks (IJCNN) (2017)
Liu, Z., Loo, C.K., Masuyama, N., Pasupa, K.: Multiple steps time series prediction by a novel Recurrent Kernel Extreme Learning Machine approach. In: 2017 9th International Conference on Information Technology and Electrical Engineering (ICITEE), Phuket (2017)
Verstraeten, D., Schrauwen, B., d’Haene, M., Stroobandt, D.: An experimental unification of reservoir computing methods. Neural Netw. 20(3), 391–403 (2007)
OrtÃn, S., Soriano, M.C., Pesquera, L., Brunner, D., San-MartÃn, D., Fischer, I., Mirasso, C.R., Gutiérrez, J.M.: A unified framework for reservoir computing and extreme learning machines based on a single time-delayed neuron. Sci. Rep. 5, 14945 (2015)
Tofallis, C.: A better measure of relative prediction accuracy for model selection and model estimation. J. Oper. Res. Soc. 66(8), 1352–1362 (2015)
Pontius, R.G., Thontteh, O., Chen, H.: Components of information for multiple resolution comparison between maps that share a real variable. Environ. Ecol. Stat. 15(2), 111–142 (2008)
Hyndman, R.J., Koehler, A.B.: Another look at measures of forecast accuracy. Int. J. Forecast. 22(4), 679–688 (2006)
Acknowledgments
We would like to extend our gratitude to the UM Grand Challenge Project ICT Project No GC003A-14HTM and the Developmental Cognitive Robot with Life-long Learning under Fund No. IF017-2018 for funding this research project.
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Abrar, S., Tahir, G.A., Kakudi, H.A., Loo, C.K. (2020). A Personalized Blood Pressure Prediction Model Using Recurrent Kernel Extreme Reservoir Machine. In: Arai, K., Bhatia, R. (eds) Advances in Information and Communication. FICC 2019. Lecture Notes in Networks and Systems, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-030-12388-8_62
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DOI: https://doi.org/10.1007/978-3-030-12388-8_62
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