Population Classification upon Dietary Data Using Machine Learning Techniques with IoT and Big Data

Abstract

In this digital age, data is generated monstrous from diverse sources like IoT enabled smart gadgets, and so on worldwide very swiftly in distinctive formats. This data with the traits say volume, velocity, variety and so on referred to as big data. Since a decade, big data technologies have been utilized in most of the companies even in healthcare alongside IoT to gain treasured insights in making knowledgeable selections spontaneously to improve medical treatment particularly for patients with complicated medical history having multiple health ailments. For healthy living, after water and oxygen, diet plays a critical role in offering the strength needed to assist the life’s existence-maintaining strategies and also the vitamins needed to construct and keep all body cells. The intent of this work is to offer a framework that classifies the population into four classes based on the quality of diet they devour within 30-days of dietary recall as balanced, unbalanced, nearly balanced, and nearly unbalanced using the machine learning techniques specifically logistic regression, linear discriminant analysis (LDA), and random forest. NHANES datasets had been used to assess the proposed framework alongside the metrics accuracy, precision, etc. This framework also allows us in gathering person’s health and dietary details dynamically anytime with the voice (IoT) to find out to which food regimen the person belongs to. This could be pretty beneficial for a person, medical doctors, and dieticians as nicely.