The intake of fast food is rapidly accelerating due to the factors specifically the cost-effectiveness and being tasty, but not all individuals are aware of its harmful long-term effects on physical and mental health. On the issue of nutritionNutrition and fast food intake this is an unexplored field of research so far. In this paper, we have profoundly analyzed the relationship using Machine LearningMachine Learning models, which is a new approach for nutritionNutrition-based analysis. A general questionnaire is prepared dealing with all the factors of nutritionNutrition and the immune system. The survey was hosted on an online platform and participants were college students from MIT WPU School of Engineering. Responses were then analyzed implying onto the food-habits and eating behavior using Random ForestRandom Forest, Naive BayesNaive Bayes and Extremely Randomized Trees. According to our understanding and knowledge, this is the earliest research to include all these factors along-with Machine LearningMachine Learning algorithms especially on college students as target audience. The primary objective is to apply association, classificationClassification, and regression algorithms in order to predict BMIBMI, sickness, pre-COVID and post-COVID eating schedule and the experiments conducted during this research reveal that this method significantly improves the analysis of real-world data as compared to the traditional statistical approach with a commendable accuracy of 98%.


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    Title :

    Machine Learning for Prediction of Nutritional Psychology, Fast Food Consumption and Its Impact on Students


    Additional title:

    Lect. Notes Electrical Eng.



    Conference:

    International Conference on Robotics, Control, Automation and Artificial Intelligence ; 2022 November 24, 2022 - November 26, 2022



    Publication date :

    2023-11-18


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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