Abstract
The present study comprises a comparison of the Mel Frequency Cepstral Coefficients (MFCC), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) as feature extraction methods using ten different regression algorithms (AdaBoost, Bayesian Ridge, Decision Tree, Elastic Net, k-NN, Linear Regression, MLP, Random Forest, Ridge Regression and Support Vector Regression) to quantify the blood glucose concentration. A total of 122 participants—healthy and diagnosed with type 2 diabetes—were invited to be part of this study. The entire set of participants was divided into two partitions: a training subset of 72 participants, which was intended for model selection, and a validation subset comprising the remaining 50 participants, to test the selected model. A 3D-printed chamber for providing a light-controlled environment and a low-cost microcontroller unit were used to acquire optical measurements. The MFCC, PCA and ICA were calculated by an open-hardware computing platform. The glucose levels estimated by the system were compared to actual glucose concentrations measured by venipuncture in a laboratory test, using the mean absolute error, the mean absolute percentage error and the Clarke error grid for this purpose. The best results were obtained for MCCF with AdaBoost and Random Forest (MAE = 11.6 for both).
Highlights
The present study comprises a comparison of the Mel Frequency Cepstral Coefficients (MFCC), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) as feature extraction methods using ten different regression algorithms (AdaBoost, Bayesian Ridge, Decision Tree, Elastic Net, k-NN, Linear Regression, multi-layer perceptron (MLP), Random Forest, Ridge Regression and Support Vector Regression) to quantify the blood glucose concentration
This disease can be identified as two main types: type 1 diabetes (T1D) takes place when the pancreas barely produces a limited amount of insulin, and even sometimes it is incapable of producing any insulin
The results presented in this study showed clinically acceptable prediction errors as established by Clarke grid analysis and regarding mean absolute error (MAE) and mean absolute percentage error (MAPE) metrics, and, they are competitive in comparison to previous related works
Summary
The present study comprises a comparison of the Mel Frequency Cepstral Coefficients (MFCC), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) as feature extraction methods using ten different regression algorithms (AdaBoost, Bayesian Ridge, Decision Tree, Elastic Net, k-NN, Linear Regression, MLP, Random Forest, Ridge Regression and Support Vector Regression) to quantify the blood glucose concentration. Insulin is a hormone that is essential in the regulation of glucose concentration in the blood and how the body uses it for converting glucose into energy. This disease can be identified as two main types: type 1 diabetes (T1D) takes place when the pancreas barely produces a limited amount of insulin, and even sometimes it is incapable of producing any insulin. The adverse effects of diabetes in health have been demonstrated, and to mention only a few it could lead to cardiovascular diseases, since people with T2D are at high mortality risk of cardiovascular illnesses such as coronary heart disease or heart failure [5,6]; diabetesassociated cognitive decline and dementia [7]; kidney alterations [8]; vision impairment published maps and institutional affiliations
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