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.
Related papers
- Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations [62.132347451049455]
Scale has become a main ingredient in obtaining strong machine learning models.
In this work, we argue that scale and training research has been needlessly complex due to reliance on the cosine schedule.
We show that weight averaging yields improved performance along the training trajectory, without additional training costs, across different scales.
arXiv Detail & Related papers (2024-05-28T17:33:54Z) - Improved Generalization of Weight Space Networks via Augmentations [56.571475005291035]
Learning in deep weight spaces (DWS) is an emerging research direction, with applications to 2D and 3D neural fields (INRs, NeRFs)
We empirically analyze the reasons for this overfitting and find that a key reason is the lack of diversity in DWS datasets.
To address this, we explore strategies for data augmentation in weight spaces and propose a MixUp method adapted for weight spaces.
arXiv Detail & Related papers (2024-02-06T15:34:44Z) - Learning to Compose SuperWeights for Neural Parameter Allocation Search [61.078949532440724]
We show that our approach can generate parameters for many network using the same set of weights.
This enables us to support tasks like efficient ensembling and anytime prediction.
arXiv Detail & Related papers (2023-12-03T04:20:02Z) - MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer
Vision [119.29105800342779]
MedShapeNet was created to facilitate the translation of data-driven vision algorithms to medical applications.
As a unique feature, we directly model the majority of shapes on the imaging data of real patients.
Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks.
arXiv Detail & Related papers (2023-08-30T16:52:20Z) - WUDI: A Human Involved Self-Adaptive Framework to Prevent Childhood
Obesity in Internet of Things Environment [25.046936884407017]
A self-adaptive framework is proposed to prevent childhood obesity by using lifelog data from IoT environments.
The framework uses an ensemble-based learning model to predict obesity using the lifelog data.
The proposed framework can be applied in real-world healthcare services for childhood obesity.
arXiv Detail & Related papers (2023-08-30T10:52:00Z) - DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep
Learning Framework [27.82565790353953]
Childhood and adolescent obesity rates are a global concern because obesity is associated with chronic diseases and long-term health risks.
This study emphasizes the importance of early identification and prevention of obesity-related health issues.
Factors such as height, weight, waist circumference, calorie intake, physical activity levels, and other relevant health information need to be considered for developing robust algorithms for obesity rate prediction.
arXiv Detail & Related papers (2023-08-28T15:40:31Z) - OBESEYE: Interpretable Diet Recommender for Obesity Management using
Machine Learning and Explainable AI [0.0]
Obesity, the leading cause of many non-communicable diseases, occurs mainly for eating more than our body requirements.
It is difficult to figure out the exact quantity of each nutrient because nutrients requirement varies based on physical and disease conditions.
We proposed a novel machine learning based system to predict the amount of nutrients one individual requires for being healthy.
arXiv Detail & Related papers (2023-08-05T06:02:28Z) - Body Fat Estimation from Surface Meshes using Graph Neural Networks [48.85291874087541]
We show that triangulated body surface meshes can be used to accurately predict VAT and ASAT volumes using graph neural networks.
Our methods achieve high performance while reducing training time and required resources compared to state-of-the-art convolutional neural networks in this area.
arXiv Detail & Related papers (2023-07-13T10:21:34Z) - Machine Learning and Bioinformatics for Diagnosis Analysis of Obesity
Spectrum Disorders [0.0]
The number of obese patients has doubled due to sedentary lifestyles and improper dieting.
Life expectancy dropped from 80 to 75 years, as obese people struggle with different chronic diseases.
This report will address the problems of obesity in children and adults using ML datasets to feature, predict, and analyze the causes of obesity.
arXiv Detail & Related papers (2022-08-05T13:07:27Z) - AutoML-Zero: Evolving Machine Learning Algorithms From Scratch [76.83052807776276]
We show that it is possible to automatically discover complete machine learning algorithms just using basic mathematical operations as building blocks.
We demonstrate this by introducing a novel framework that significantly reduces human bias through a generic search space.
We believe these preliminary successes in discovering machine learning algorithms from scratch indicate a promising new direction in the field.
arXiv Detail & Related papers (2020-03-06T19:00:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.