Noninvasive estimation of blood glucose and HbA1c using Quantum Machine Learning technique

Abstract

In this paper, we developed models with quantum and classical machine learning algorithms to detect blood glucose and HbA1c noninvasively from ten-second fingertip video by deploying a smartphone and near-infrared spectroscopy. Using our developed framework, we collected 136 participants’ ten-second fingertip videos with their baseline blood glucose and HbA1c levels after getting approval from the Institutional Review Board (IRB). We extracted 45 PPG (photoplethysmography) features from the ten-second fingertip video by using the Beer–Lambert law and applied feature engineering to select the most important features. We applied two Quantum Machine Learning (QML) based algorithms and seven Classical Machine Learning (CML) based algorithms for estimating blood glucose and HbA1c levels. The application of QML for the noninvasive estimation of blood glucose and HbA1c is a new and unexplored research area. Among all developed models, the Quantum Support Vector Machine performs best for predicting both blood glucose and HbA1c. The Quantum Support Vector Machine provides an accuracy of 89.30% and an average k-fold cross-validation score of 92.50% for blood glucose prediction and an accuracy of 96.30% and an average k-fold cross-validation score of 92.50% for HbA1c prediction. Our study signifies the potential of QML algorithms in noninvasive health monitoring, especially in the less-explored area of blood glucose and HbA1c estimation. The high performance of the developed models paves the way for advancing noninvasive techniques for measuring blood constituents. These findings offer promising applications in personalized healthcare, including continuous monitoring, early disease diagnosis, and more convenient management of chronic conditions.