MOFit: A Framework to reduce Obesity using Machine learning and IoT
- URL: http://arxiv.org/abs/2108.08868v1
- Date: Thu, 19 Aug 2021 18:26:51 GMT
- Title: MOFit: A Framework to reduce Obesity using Machine learning and IoT
- Authors: Satvik Garg and Pradyumn Pundir
- Abstract summary: sedentary living style in urban areas is at its peak. This results in individuals getting a victim of obesity at an early age.
In this work, we aim to provide a framework that uses machine learning algorithms to train models that would help predict obesity levels.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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