MOFit: A Framework to reduce Obesity using Machine learning and IoT

TL;DR

This paper presents MOFit, a comprehensive framework combining machine learning and IoT to predict obesity-related metrics, provide personalized health plans, and track progress, aiming to combat obesity in urban populations.

Contribution

It introduces a novel integrated framework that uses multiple ML algorithms and IoT devices for obesity prediction, personalized diet and workout plans, and progress tracking.

Findings

ML models achieved high accuracy in predicting obesity levels.

Hyperparameter optimization improved model performance.

IoT integration enabled real-time food intake monitoring.

Abstract

From the past few years, due to advancements in technologies, the sedentary living style in urban areas is at its peak. This results in individuals getting a victim of obesity at an early age. There are various health impacts of obesity like Diabetes, Heart disease, Blood pressure problems, and many more. Machine learning from the past few years is showing its implications in all expertise like forecasting, healthcare, medical imaging, sentiment analysis, etc. In this work, we aim to provide a framework that uses machine learning algorithms namely, Random Forest, Decision Tree, XGBoost, Extra Trees, and KNN to train models that would help predict obesity levels (Classification), Bodyweight, and fat percentage levels (Regression) using various parameters. We also applied and compared various hyperparameter optimization (HPO) algorithms such as Genetic algorithm, Random Search, Grid Search, Optuna to further improve the accuracy of the models. The website framework contains various other features like making customizable Diet plans, workout plans, and a dashboard to track the progress. The framework is built using the Python Flask. Furthermore, a weighing scale using the Internet of Things (IoT) is also integrated into the framework to track calories and macronutrients from food intake.