Machine Learning based Food Ingredients Classification with early chronic disease prediction

Abstract

For the past decade, many health, nutrition, and diet guiding apps have been introduced for the web-based and mobile - based applications, and they mostly focus on providing the ability to access all prescriptions, diets, counting calories, tracking nutrition, tracking bowel diseases, allergies, and individual workout plans and less on predicting longtime affection that can be happened because of the consuming foods. Due to this reason, the long-term impact of eating unhealthy has been forgotten. This research looks depth in counting calories and tracking the nutrition of consumed foods and predict the food-related chronic diseases through continuing the current food patterns. In this application, it allows the user to enter the food and the quantities that they consume for a day. Then according to the wide food ingredients database, the unit ingredients and the total calories for the very foods will be classified. The amount of included food ingredients as percentages and the calories will be calculated and then the relevant food chronic diseases will be predicted. For that, the daily standard intake of particular ingredients is compared with the food intake that the user consumed.