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A Personalized Blood Pressure Prediction Model Using Recurrent Kernel Extreme Reservoir Machine

  • Sundus Abrar
  • Ghalib Ahmad Tahir
  • Habeebah Adamu Kakudi
  • Chu Kiong LooEmail author
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 69)

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.

Keywords

Hypertension Blood pressure Prediction ELM RKERM Personalized prediction model 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Sundus Abrar
    • 1
  • Ghalib Ahmad Tahir
    • 1
  • Habeebah Adamu Kakudi
    • 1
  • Chu Kiong Loo
    • 1
    Email author
  1. 1.University of MalayaKuala LumpurMalaysia

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