Abstract
This paper deals with a new approach of non-invasive glucose monitoring based on near infrared spectroscopy. The proposed approach is coupled with a regression analysis in order to improve the predictive capacity of the designed device. Basic spectral data is a comparison that has been established between linear and non-linear machine learning regression algorithms. The experimental results show that feed forward backpropagation neural network improves more the performance of the designed prototype than partial least square models. The squared correlation coefficient and the Root Mean Square Error (RMSE) of the Artificial Neural Network (ANN) regression model built were 0.9804 and 0.0784 respectively. The ANN regression model was then used in the validation step using 300 human serums with a concentration range of 08-297 mg/dl. Clarke Error Grid Analysis (EGA) showed that 97% of the measured concentrations fall within the clinically acceptable regions. Results showed that the created model can open a new path to a non-invasive glucose monitoring.
Highlights
Diabetes has become an epidemic affecting more than 422 million people worldwide (WHO, 2018)
We will evaluate the performance of the Partial Least Squares (PLS) models within its linear and polynomial forms. 24 samples were used as a calibration dataset
The R squared and the correlation coefficient for linear PLS are 0.9605 and 0.9792 respectively, which shows a good relationship between the real blood glucose concentration values and the linear PLS prediction values
Summary
Diabetes has become an epidemic affecting more than 422 million people worldwide (WHO, 2018). Diabetes is a metabolic disorder, caused by a dysfunction in the insulin section. Its interaction with glucose allows the body’s cellular energy. The problem is that the patient's body does not produce insulin accurately throughout the day, the importance of insulin to regulate blood sugar level, avoiding making it too high or too low (Joseph and Golden, 2014). Diabetes is a degenerative disease that without treatment can have very serious consequences, such as blindness, kidney failure and nerve damage. There can be a tooth and gum damage, skin disease, thyroid problem and sexual dysfunction (ADA, 2011)
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