Diabetes Risk Assessment: A Comparative Study of Decision Trees and Ensemble Learning Models
Diabetes poses a significant threat to global health, making accurate prediction and effective treatment of the disease critical. This study explores the application of machine learning algorithms in assessing diabetes risk, with a particular focus on Decision Trees (DT) and Ensemble Learning techni...
Saved in:
Main Author: | Lei Tianxing |
---|---|
Format: | Article |
Language: | English |
Published: |
EDP Sciences
2025-01-01
|
Series: | ITM Web of Conferences |
Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_02020.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Evaluation of Machine Learning and Ensemble Learning Models for Classification Using Delivery Data
by: İrem Karakaya
Published: (2025-02-01) -
Application Of ArtifiCial Intelligence in E-Governance: A Comparative Study of Supervised Machine Learning and Ensemble Learning Algorithms on Crime Prediction.
by: Niyonzima, Ivan, et al.
Published: (2024) -
Enhancing Medical Diagnostics with Machine Learning: A Study on Ensemble Methods and Transfer Learning
by: Zhang Jiaming
Published: (2025-01-01) -
A deep ensemble learning framework for glioma segmentation and grading prediction
by: Liang Wen, et al.
Published: (2025-02-01) -
Comparative Analysis of Diabetes Prediction Models Using the Pima Indian Diabetes Database
by: Zhao Yize
Published: (2025-01-01)