Non-invasive glucose prediction and classification using NIR technology with machine learning

M Naresh; V Siva Nagaraju; Sreedhar Kollem; Jayendra Kumar; Samineni Peddakrishna

doi:10.1016/j.heliyon.2024.e28720

Non-invasive glucose prediction and classification using NIR technology with machine learning

Heliyon. 2024 Mar 28;10(7):e28720. doi: 10.1016/j.heliyon.2024.e28720. eCollection 2024 Apr 15.

Authors

M Naresh¹, V Siva Nagaraju², Sreedhar Kollem³, Jayendra Kumar¹, Samineni Peddakrishna¹

Affiliations

¹ School of Electronics Engineering, VIT-AP University, Amaravti, Guntur, 522241, Andhra Pradesh, India.
² Department of ECE, Institute of Aeronautical Engineering, Dundigal, Hyderabad, 500043, Telangana, India.
³ Department of ECE, School of Engineering, SR University, Warangal, 506371, Telangana, India.

Abstract

In this paper, a dual wavelength short near-infrared system is described for the detection of glucose levels. The system aims to improve the accuracy of blood glucose detection in a cost-effective and non-invasive way. The accuracy of the method is evaluated using real-time samples collected with the reference finger prick glucose device. A feed forward neural network (FFNN) regression method is employed to predict glucose levels based on the input data obtained from NIR technology. The system calculates glucose evaluation metrics and performs Surveillance error grid (SEG) analysis. The coefficient of determination $R^{2}$ and mean absolute error are observed 0.99 and 2.49 mg/dl, respectively. Additionally, the system determines the root mean square error (RMSE) as 3.02 mg/dl. It also shows that the mean absolute percentage error (MAPE) is 1.94% and mean squared error (MSE) is 9.16 ${(m g / d l)}^{2}$ for FFNN. The SEG analysis shows that the glucose values measured by the system fall within the clinically acceptable range when compared to the reference method. Finally, the system uses the multi-class classification method of the multilayer perceptron (MLP) and K-nearest neighbors (KNN) classifier to classify glucose levels with an accuracy of 99%.

Keywords: Absorbance; Classification; Detectors; Glucose; Infrared; Machine learning; Noninvasive; Regression; Spectroscopy.