发现2型糖尿病与相关性和优化驱动的混合深度学习方法
Karuna Middha1, Apeksha Mittal1
1Department of CSE, School of Engineering and Science, GD Goenka University, Sohna, Haryana, India.
Computer methods in biomechanics and biomedical engineering
|October 22, 2023
概括
这项研究引入了一种混合深度学习模型,用于准确检测2型糖尿病 (T2DM). 这种新的方法实现了高精度,有助于早期诊断和治疗这种慢性疾病.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 计算生物学 计算生物学
背景情况:
- 糖尿病是一种慢性疾病,其特征是血糖水平升高.
- 未被发现的糖尿病会导致严重的器官损伤.
- 准确和早期诊断2型糖尿病 (T2DM) 对于有效管理至关重要.
研究的目的:
- 开发和评估一种混合深度学习技术,用于识别2型糖尿病.
- 使用先进的计算方法提高T2DM诊断的准确性和效率.
主要方法:
- 数据预处理涉及Yeo-Johnson转换用于数据清理.
- 坦尼莫托相似性被用于最佳特征选择.
- 结合深度剩余网络 (DRN) 和基于骑手的神经网络 (RideNN) 的混合模型使用竞争多节骑手优化器 (CMVRO) 进行训练.
- 基于关联的融合被用来结合DRN和RideNN分类器的输出.
主要成果:
- 拟议的基于CMVRO的NN-DRN模型在T2DM识别中表现出卓越的性能.
- 该模型的精度为91.4%,灵敏度为94.8%,特异性为90.1%.
结论:
- 混合深度学习方法为准确的T2DM检测提供了一个有希望的工具.
- 基于CMVRO的NN-DRN模型在糖尿病的计算诊断方面取得了重大进展.
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