Diet Recommendation using Predictive Learning Approaches

Abstract

In the modern world, people's health is one of their top priorities. As a result of inadequate nutrition, many people in the modern world are afflicted with a variety of diseases. It is urgently necessary to make timely and easy recommendations for a balanced diet, which is quite challenging. Time is one of the major restraints in today's world, therefore authors created a system for recommending diets to people based on their unique health needs, whether they are trying to gain weight, lose weight, or get a health checkup. The system is built on machine learning classifiers including Random Forest, Support Vector Machine, Ada Boost, and Gradient, as well as clustering methods like K-means. Boost. After calculating Body Mass Index and the user's desire for diet based on their health state (overweight, underweight, or healthy), the suggested system makes meal recommendations based on the user's age, height, and weight. The proposed paper summarises numerous similar works and evaluates the effectiveness of the suggested strategy. The performance comparison is displayed in terms of recall, recall accuracy, precision, and f1-measure.