Breath Analysis Using Quartz Tuning Forks for Predicting Blood Glucose Levels Using Artificial Neural Networks

Abstract

Diabetes Mellitus (DM), a widespread metabolic disorder, poses lifelong health implications, demanding timely diagnosis and cautious monitoring for effective disease management. Traditional blood glucose tests are invasive and require medical expertise for intermittent checking, motivating the investigation of alternative, noninvasive methods. This study introduces an approach employing breath analysis through a set of 12 quartz tuning fork-based sensors enhanced using nanomaterials and dedicated artificial neural network (ANN) algorithms for data interpretation. The breath analysis methodology involves capturing unique breath signatures using the frequency-based sensor array. The accompanying neural network classification algorithm, customized for the sensor data, enables precise classification of data from 245 individuals as diabetic, prediabetic, or healthy. A neural network regression algorithm predicted blood glucose values and was compared with the actual values obtained from medical blood glucose measurement. The clinical relevance of the predicted blood glucose has been examined using error grids. The sensor array coupled with the ANN algorithm can identify diabetic, prediabetic, and control samples with 97% test accuracy. Blood glucose was predicted using neural network regression with a correlation coefficient of 0.89 and a mean square error of 0.13.