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使用深度学习进行儿科糖尿病预测

Abeer El-Sayyid El-Bashbishy¹, Hazem M El-Bakry²

¹Information Systems Department, Faculty of Computer and Information Sciences, Mansoura University, Mansoura, Egypt. abeerelbashbishy@gmail.com.

Scientific reports

|February 20, 2024

概括

这项研究引入了一种新的深度学习 (DL) 技术,用于早期糖尿病预测. 开发的深度神经网络 (DNN) 模型实现了99.8%的准确性,改善了糖尿病诊断的现有方法.

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:

早期预测糖尿病对于有效管理和预防并发症至关重要.
深度学习 (DL) 模型在加速糖尿病诊断方面表现有前途.
现有的方法需要优化,以提高准确性和效率.

研究的目的:

通过深度学习提出一种新的技术,用于高精度的早期糖尿病预测.
开发和验证用于糖尿病诊断的深度神经网络 (DNN) 模型.
通过细致的超参数调来优化DL模型的性能.

主要方法:

实现一个深度神经网络 (DNN),有十个隐藏层和多个时代.
使用基于深度神经网络 (DNN) 的多层感知器 (MLP) 算法.
微调超参数用于MUCHD数据集上的自动化数据预处理,预测和分类.
使用交叉验证与包括精度,F-score,精度,灵敏度,特异性和Dice相似系数在内的指标进行验证,对548名具有18个特征的患者进行验证.

主要成果:

拟议的DL系统在糖尿病预测方面实现了99.8%的显著准确率.
该模型显示,与最先进的方法相比,系统整体性能增加了0.39%.
使用多个指标的严格验证证实了预测系统的可靠性和高性能.

结论:

开发的DL技术为早期糖尿病预测提供了一个高度准确和可靠的方法.
拟议的基于DNN的MLP模型显示了在糖尿病诊断中临床应用的巨大潜力.
这种新的方法代表了利用人工智能的进步,以改善糖尿病的护理和管理.

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