A Hybrid Machine Learning-Based Nutrition Recommendation System for Personalized Dietary Plans

Abstract

In the modern health landscape, personalized nutrition is crucial for promoting well-being and managing chronic diseases. As we all move forward with our modern and technologically oriented lifestyle, the risk of suffering from various health ailments is very high. In this study, a hybrid system of nutrition recommendations, a combination of machine learning, data extraction, and visualization, is introduced to offer a personalized dietary solution. The system takes Body Mass Index (BMI), and incorporates medical history, food preferences, allergies and calorie consumption to produce custom meal plans. This platform increases the adherence to the diet, user-interaction, and wellbeing in the long term by providing an interactive graphical user interface and real-time data visualization.

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