Enhancing Diabetes Prediction and Management Through Machine Learning Innovations

DOI

10.2991/978-94-6463-540-9_7How to use a DOI?

Keywords

Diabetes; Machine Learning; Diabetes Prediction

Abstract

Nowadays many people are suffering from diabetes. This disease poses significant health risks and economic burdens. This research addresses the increasing incidence of diabetes by investigating the use of machine learning to improve diabetes treatment, diagnosis, and prediction. The research shows how machine learning models can evaluate complicated datasets and incorporate elements like genetic predispositions and lifestyle decisions to increase prediction accuracy. These models are based on techniques like decision trees, random forests, and neural networks. Key findings include the superior performance of the XGBoost classifier combined with Adaptive Synthetic Sampling (ADASYN), achieving 81% accuracy in predicting diabetes risk. By enabling proactive treatments and optimal management regimens, this research highlights the effectiveness of machine learning in early detection and individualized treatment, thereby providing a new approach to diabetes care. The integration of machine learning in healthcare promises significant improvements in patient outcomes and a reduction in healthcare costs, marking a pivotal advancement in combating diabetes globally.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zidie Zhuang
PY  - 2024
DA  - 2024/10/16
TI  - Enhancing Diabetes Prediction and Management Through Machine Learning Innovations
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 53
EP  - 59
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_7
DO  - 10.2991/978-94-6463-540-9_7
ID  - Zhuang2024
ER  -