A Review on Supervised Learning Methodologies for Detecting Eating Habits of Diabetic Patients

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%.