Key result
Machine learning models using non-invasive wearable data accurately estimate blood glucose levels.
Why the study?
Blood glucose levels are often monitored invasively, and understanding the relationships between non-invasive wearable device features and glycemic health markers is needed to evaluate model accuracy in estimating blood glucose levels.
Do machine learning models using non-invasive wearable device data accurately estimate blood glucose levels in diabetic patients?
Observational (n=13)
Do machine learning models using non-invasive wearable device data accurately estimate blood glucose levels in diabetic patients?
Machine learning models using non-invasive wearable device data can accurately estimate blood glucose levels, demonstrating the feasibility of this approach for diabetes monitoring.
Supports feasibility of wearable glucose estimation in small cohorts; leaves open larger validation before clinical use.
In 2019 alone, Diabetes Mellitus impacted 463 million individuals worldwide. Blood glucose levels (BGL) are often monitored via invasive techniques as part of routine protocols. Recently, AI-based approaches have shown the ability to predict BGL using data acquired by non-invasive Wearable Devices (WDs), therefore improving diabetes monitoring and treatment. It is crucial to study the relationships between non-invasive WD features and markers of glycemic health. Therefore, this study aimed to investigate accuracy of linear and non-linear models in estimating BGL. A dataset containing digital metrics as well as diabetic status collected using traditional means was used. Data consisted of 13 participants data collected from WDs, these participants were divided in two groups young, and Adult Our experimental design included Data Collection, Feature Engineering, ML model selection/development, and reporting evaluation of metrics. The study showed that linear and non-linear models both have high accuracy in estimating BGL using WD data (RMSE range: 0.181 to 0.271, MAE range: 0.093 to 0.142). We provide further evidence of the feasibility of using commercially available WDs for the purpose of BGL estimation amongst diabetics when using Machine learning approaches.
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Aziz et al. (2023) conducted an observational in Diabetes Mellitus (n=13). Machine learning models using non-invasive wearable device data vs. Traditional invasive blood glucose monitoring was evaluated on Accuracy of linear and non-linear models in estimating blood glucose levels (RMSE and MAE). Machine learning models using non-invasive wearable device data accurately estimated blood glucose levels, achieving an RMSE range of 0.181 to 0.271 and MAE range of 0.093 to 0.142.
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