Abstract
Diabetes is a chronic metabolic disease characterized by high blood sugar levels, which over time leads to body complications that can affect the heart, blood vessels, eyes, kidneys, and nerves. To control this disease, the use of applications for tracking and monitoring vital signs have been used frequently. These support systems improve their quality of life and prevent exacerbations, however they cannot help with nutritional control, so several patients with this disease still use the counting carbohydrates method, but this process is not available to everyone and is a time-consuming and not very rigorous method. This study evaluates three approaches including Support Vector Machine, Convolution Neural Network, and a pre-trained Convolution Neural Network called MobileNetV2, to choose the algorithm with the best performance in meals recognition and makes the control nutritional task more quickly, accurately, and efficiently. The results showed that the pre-trained Convolution Neural Network is the best choice for recognizing meals from an image, with an accuracy of 99%.
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Acknowledgements
This research work was developed under the project Food Friend –“Autonomous and easy-to-use tool for monitoring of personal food intake and personalized feedback” (ITEA 18032), co-financed by the North Regional Operational Program (NORTE 2020) under the Portugal 2020 and the European Regional Development Fund (ERDF), with the reference NORTE-01-0247-FEDER-047381 and by National Funds through FCT (Fundação para a Ciência e a Tecnologia) under the project UI/BD/00760/2020.
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Antelo, C., Martinho, D., Marreiros, G. (2022). A Review on Supervised Learning Methodologies for Detecting Eating Habits of Diabetic Patients. In: Marreiros, G., Martins, B., Paiva, A., Ribeiro, B., Sardinha, A. (eds) Progress in Artificial Intelligence. EPIA 2022. Lecture Notes in Computer Science(), vol 13566. Springer, Cham. https://doi.org/10.1007/978-3-031-16474-3_31
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