Nutritional Analysis Using Convolutional Neural Network for Type II Diabetes

Abstract

The most prevalent disease is type 2 diabetes mellitus (T2DM), a chronic metabolic disorder. T2DM is linked to fat buildup in the lower torso around the abdomen, which leads to fat buildup in the belly region. As a result, it’s important to categorize and forecast diabetes patients based on their dietary intake. In this study, we used the pre-trained Inception V3, Keras, and Tensorflow convolutional neural network (CNN) model to identify different food categories. Comparing the CNN model’s accuracy to other methods from earlier studies, it achieved 96.6%, which is fairly high. Additionally, there is a correlation between calories with fat, carbs, protein, and sugar related with T2DM via linear regression between nutrition classes.