Key result
Machine learning using HRV and pulse oximetry predicts high blood glucose with ~82% accuracy.
Why the study?
Standard blood glucose meters are invasive and often expensive, motivating non-invasive, cost-effective alternatives using heart rate variability and pulse oximetry.
Do machine learning models using heart rate variability and pulse oximeter data accurately predict high blood glucose levels?
Do machine learning models using heart rate variability and pulse oximeter data accurately predict high blood glucose levels?
Machine learning models utilizing non-invasive heart rate variability and pulse oximetry data demonstrate promising accuracy in predicting high blood glucose levels.
As the current literature shows, there is a correlation of Heart Rate Variability (HRV) and the levels of glucose in blood (BGL), which can be used when assessing diabetes related risks. Commonly, a blood glucose meter is used as an invasive, and often expensive, tool to measure BGL at multiple times of day; however, there are studies showing non-invasive solutions that use the principles of HRV instead. Measuring BGL with a pulse oximeter and HRV is a beneficial, cost-effective, non-invasive method that represents an easier, less-frightening approach. We developed a methodology based on information obtained from our previous works, to create predictive models based on machine learning methods such as Twin Support Vector Machines (TWSVM) and boosted Bayesian Additive regression Trees (xBART). Utilizing a total of 58 variables generated by HRV variables and pulse oximeter information, we computed cross-validation tests, and a general train-test split for both models. The performance of the models was computed with balanced accuracy and weighted F1-score metrics, resulting in promising predictions that ranged upwards of 72.2% to 82.1% for the accuracy. As for the weighted F1-score, our results ranged from 75.0% to 76.5%.
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Martinez-Delgado et al. (2022) studied High blood glucose. Machine learning models (TWSVM and xBART) using HRV and pulse oximeter data was evaluated on Balanced accuracy and weighted F1-score. Machine learning models utilizing heart rate variability and pulse oximeter data predicted high blood glucose levels with balanced accuracy ranging from 72.2% to 82.1% and F1-scores up to 76.5%.
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