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Predicting and Managing Glycemia Levels Using Advanced Time Series Forecasting Methods

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Intelligent Healthcare

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

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Abstract

About 1 in 11 people around the world have some kind of diabetes, which is one of the leading causes of death. The two major types of diabetes which are prominent are type 1 and type 2 diabetes, where about 90% of the total cases are related to type 2. Fortunately, people with diabetes can lead long and healthy lives when their diabetes is diagnosed early, well managed, and self-monitored. Self-monitoring data can help people manage their diabetes and prevent hyperglycemic (high glucose levels) and hypoglycemic (low glucose levels) conditions. It is important to note that glucose-level fluctuations vary from individual to individual based on their current condition and other biological factors. In this research, we propose a methodology to manage diabetes using data from continuous glucose monitoring (CGM) devices and predicting glucose levels after a certain activity like intake of meals, intensive workouts, sleep, etc. takes place. The methodology comprises (1) analysis of ARIMAX and FbProphet time series forecasting techniques to predict future glycemic levels on the Nightscout dataset and (2) sheds some light over the interpretability of other parameters such as carbohydrates and insulin. The experimental results show that the FbProphet model outperforms the ARIMAX model.

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Author information

Authors and Affiliations

  1. Koverhoop Technologies India, Noida, India

    Sahil Malhotra

  2. Department of CSE, The NorthCap University, Gurugram, India

    Rita Chhikara

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  1. Sahil Malhotra
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  2. Rita Chhikara
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Correspondence to Sahil Malhotra .

Editor information

Editors and Affiliations

  1. Computer Sciences and Information Technology, King Faisal University, Alhasa, Saudi Arabia

    Surbhi Bhatia

  2. Computer Science and Engineering, Chitkara University, Rajpura, Punjab, India

    Ashutosh Kumar Dubey

  3. Computer Science and Engineering, The North Cap University, Gurugram, Haryana, India

    Rita Chhikara

  4. Computer Science and Engineering, The North Cap University, Gurugram, Haryana, India

    Poonam Chaudhary

  5. Computer Science and Engineering, Chitkara University, Rajpura, Punjab, India

    Abhishek Kumar

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Malhotra, S., Chhikara, R. (2021). Predicting and Managing Glycemia Levels Using Advanced Time Series Forecasting Methods. In: Bhatia, S., Dubey, A.K., Chhikara, R., Chaudhary, P., Kumar, A. (eds) Intelligent Healthcare. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-67051-1_9

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  • DOI: https://doi.org/10.1007/978-3-030-67051-1_9

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  • Print ISBN: 978-3-030-67050-4

  • Online ISBN: 978-3-030-67051-1

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