Analysis of Health Status of Alcoholic Students Based on the Gradient Boosting Decision Tree

Yijie Tang, Yixiang Yang, Baoshu Zhao

2024

Abstract

Underage alcohol consumption has become an increasingly serious issue in contemporary society. Not only does it violate legal regulations, but it may also lead to a series of severe social problems, such as poor academic performance and mental health issues. Therefore, conducting comprehensive and in-depth research on underage alcohol consumption can help better understand its influencing factors and potential consequences, thus taking corresponding measures to prevent and address them. To address this issue, this study employs the Gradient Boosting Decision Tree (GBDT) model for deep learning and prediction using a dataset of students' academic performance. The analysis includes insights into the features and attributes based on GBDT, as well as an examination of the relationship between alcohol consumption and other characteristics. The results reveal that among students engaged in alcohol consumption, 25% ultimately achieve unsatisfactory academic performance. Simultaneously, 50% of students with the lowest alcohol consumption enter the next semester with the lowest grades. However, students consuming moderate amounts of alcohol on weekdays experience consistent failure in both aspects.

Paper Citation

in Harvard Style

Tang Y., Yang Y. and Zhao B. (2024). Analysis of Health Status of Alcoholic Students Based on the Gradient Boosting Decision Tree. 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 30-39. DOI: 10.5220/0012889600004508

in Bibtex Style

@conference{emiti24,
author={Yijie Tang and Yixiang Yang and Baoshu Zhao},
title={Analysis of Health Status of Alcoholic Students Based on the Gradient Boosting Decision Tree},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={30-39},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012889600004508},
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 - Analysis of Health Status of Alcoholic Students Based on the Gradient Boosting Decision Tree
SN - 978-989-758-713-9
AU - Tang Y.
AU - Yang Y.
AU - Zhao B.
PY - 2024
SP - 30
EP - 39
DO - 10.5220/0012889600004508
PB - SciTePress