Automatic prediabetes prediction using heart rate variability (Preprint) Article Swipe
Jeban Chandir Moses
,
Sasan Adibi
,
Sheikh Mohammed Shariful Islam
,
Maia Angelova
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.2196/preprints.50972
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.2196/preprints.50972
BACKGROUND Approximately 25% of prediabetics progress to overt type 2 diabetes within 3 to 5 years and 70% develop overt diabetes in their lifetime. Prediabetics could be identified through screening, which could reduce the healthcare burden. HRV is an index of the autonomic nervous system and serves as a measurable indicator for various chronic diseases. Commercial wearable devices have the potential to capture HRV in non-clinical settings. OBJECTIVE This study evaluates if machine learning techniques applied to HRV data captured in non-clinical settings could be used as a non-invasive biomarker to classify healthy adults and those with elevated blood glucose levels. METHODS Four machine learning classification algorithms: support vector machine (SVM), k-Nearest Neighbours (KNN), Naive Bayes (NB), and Decision Tree (DT), was applied to the computed HRV parameters to perform classification. RESULTS The overall best performance accuracy of 80% was achieved by KNN, and DT trained on HRV data with a time window length of 5 min. The study observed that HRV parameters computed from wearables in non-clinical settings could classify healthy adults and those with elevated blood glucose levels with acceptable accuracy. CONCLUSIONS The findings of this study could inform the use of machine learning approaches with wearable device data to screen prediabetes individuals.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.2196/preprints.50972
- OA Status
- gold
- References
- 1
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- 10
- OpenAlex ID
- https://openalex.org/W4385278247
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https://openalex.org/W4385278247Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.2196/preprints.50972Digital Object Identifier
- Title
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Automatic prediabetes prediction using heart rate variability (Preprint)Work title
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preprintOpenAlex work type
- Language
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enPrimary language
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2023Year of publication
- Publication date
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2023-07-18Full publication date if available
- Authors
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Jeban Chandir Moses, Sasan Adibi, Sheikh Mohammed Shariful Islam, Maia AngelovaList of authors in order
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https://doi.org/10.2196/preprints.50972Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.2196/preprints.50972Direct OA link when available
- Concepts
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Prediabetes, Artificial intelligence, Support vector machine, Naive Bayes classifier, Wearable computer, Machine learning, Medicine, Computer science, Heart rate variability, Diabetes mellitus, Internal medicine, Type 2 diabetes, Heart rate, Blood pressure, Endocrinology, Embedded systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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1Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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