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Estimating Blood Glucose Levels Using Machine Learning Models with Non-Invasive Wearable Device Data.

Sarah Aziz¹, Arfan Ahmed¹, Alaa Abd-Alrazaq¹

¹AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.

Studies in Health Technology and Informatics

|June 30, 2023

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Non-invasive wearable devices can accurately predict blood glucose levels (BGL) in individuals with diabetes using AI. This technology offers a more convenient method for diabetes management and monitoring.

Area of Science:

Biomedical Engineering
Artificial Intelligence
Endocrinology

Background:

Diabetes Mellitus affects millions globally, necessitating improved monitoring methods.
Current blood glucose level (BGL) monitoring often involves invasive techniques.
Non-invasive methods using wearable devices (WDs) show promise for BGL prediction.

Purpose of the Study:

To investigate the accuracy of linear and non-linear models in estimating BGL.
To explore the relationship between non-invasive WD features and glycemic health markers.
To assess the feasibility of using WDs for BGL estimation in diabetic individuals.

Main Methods:

Data collection from 13 participants (young and adult groups) using WDs.
Feature engineering and machine learning (ML) model selection and development.

Keywords:

Artificial Intelligence Blood glucose level Deep learning Diabetes Machine learning Wearable devices

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Evaluation of model performance using metrics like RMSE and MAE.

Main Results:

Both linear and non-linear ML models demonstrated high accuracy in BGL estimation.
Achieved Root Mean Square Error (RMSE) range: 0.181 to 0.271.
Achieved Mean Absolute Error (MAE) range: 0.093 to 0.142.

Conclusions:

Commercially available WDs can be effectively used for BGL estimation in diabetics with ML approaches.
Provides evidence supporting the non-invasive monitoring of glycemic health.
Highlights the potential of AI and WDs to enhance diabetes care and treatment.