Machine-Learning-Based Prediction of Obesity

Siyuan Chen

2024

Abstract

Obesity is a common phenomenon today. It is a chronic metabolic disease caused by excessive fat accumulation and is the result of the interaction of multiple factors such as genetics and environment. As machine learning is widely used in various fields, obesity data is processed by using machine learning methods to obtain fitting models, to realize the prediction of obesity and to determine the main causes of obesity. The main contents of this paper include: (1) using the obesity data provided by the UCI data set as the research object, using a series of preprocessing data in Python language. (2) Establish a machine learning model and import the data to generate bar charts and other graphs that reflect the relevant results. (3) Results analysis, to Determine which factors are more closely related to obesity and evaluate the performances of the models. The comprehensive analysis of accuracy, recall rate, precision rate and other indicators finally obtains the best prediction effect of the GBDT algorithm, which can effectively predict obesity.

Paper Citation

in Harvard Style

Chen S. (2024). Machine-Learning-Based Prediction of Obesity. In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-713-9, SciTePress, pages 153-158. DOI: 10.5220/0012916000004508

in Bibtex Style

@conference{emiti24,
author={Siyuan Chen},
title={Machine-Learning-Based Prediction of Obesity},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={153-158},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012916000004508},
isbn={978-989-758-713-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - Machine-Learning-Based Prediction of Obesity
SN - 978-989-758-713-9
AU - Chen S.
PY - 2024
SP - 153
EP - 158
DO - 10.5220/0012916000004508
PB - SciTePress