MOFit: A Framework to reduce Obesity using Machine learning and IoT
This work addresses obesity management for individuals in urban areas, but it is incremental as it applies existing methods to a new application.
The authors tackled obesity prediction and management by developing a framework that uses machine learning algorithms to predict obesity levels, body weight, and fat percentage, achieving improved accuracy through hyperparameter optimization, and integrated IoT for tracking food intake. The framework includes features like diet and workout plans, built with Python Flask.
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.