Predictive Analytics in Type-1 Diabetes Using Machine Learning Algorithms

doi:10.1007/978-3-032-05545-3_14

2026-01-01

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DOI：10.1007/978-3-032-05545-3_14

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1型糖尿病是一种终身性代谢紊乱，需要及时准确的诊断以预防严重的健康并发症。本研究探讨了机器学习和深度学习方法在提高1型糖尿病检测精度中的应用。来自Kaggle的糖尿病二分类健康数据集为本研究提供了分析基础。采用随机森林分类器进行特征选择，将原始22个特征缩减至最具影响力的15个特征。开发了五种模型——AdaBoost、随机森林、神经网络、梯度提升和k近邻，并进行了系统评估。其中，AdaBoost模型表现出卓越性能，AUC达到0.998，准确率达到98.4%。这些发现强调了计算技术在改善早期诊断中的潜力，最终有助于优化患者管理，并为1型糖尿病的医疗策略发展做出贡献。

Keyword:

Type 1 diabetes

machine learning

deep learning

AdaBoost

Random Forest

feature selection

early detection

medical diagnostics

期刊

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON AI AND ROBOTICS, AIR

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论文数:

被引数:

机构

canadian university dubai

学者数:

193

论文数: 232

被引数: 1

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