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Real-Time Statistical Modeling of Blood Sugar

  • Mobile Systems
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Abstract

Diabetes is considered a chronic disease that incurs various types of cost to the world. One major challenge in the control of Diabetes is the real time determination of the proper insulin dose. In this paper, we develop a prototype for real time blood sugar control, integrated with the cloud. Our system controls blood sugar by observing the blood sugar level and accordingly determining the appropriate insulin dose based on patient’s historical data, all in real time and automatically. To determine the appropriate insulin dose, we propose two statistical models for modeling blood sugar profiles, namely ARIMA and Markov-based model. Our experiment used to evaluate the performance of the two models shows that the ARIMA model outperforms the Markov-based model in terms of prediction accuracy.

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Authors and Affiliations

  1. Yarmouk University, Irbid, Jordan

    Mwaffaq Otoom & Hisham M. Almasaeid

  2. Jordan University of Science and Technology, Irbid, Jordan

    Hussam Alshraideh

  3. University of Deusto, Bilbao, Spain

    Diego López-de-Ipiña

  4. Castilla-La Mancha University, Ciudad Real, Spain

    José Bravo

Authors
  1. Mwaffaq Otoom
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  2. Hussam Alshraideh
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  3. Hisham M. Almasaeid
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  4. Diego López-de-Ipiña
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  5. José Bravo
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Corresponding author

Correspondence to Mwaffaq Otoom.

Additional information

This article is part of the Topical Collection on UCAmI & IWAAL 2014

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Otoom, M., Alshraideh, H., Almasaeid, H.M. et al. Real-Time Statistical Modeling of Blood Sugar. J Med Syst 39, 123 (2015). https://doi.org/10.1007/s10916-015-0301-8

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  • DOI: https://doi.org/10.1007/s10916-015-0301-8

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