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区块链启用了深度学习模型,修改了coati优化,用于可持续的医疗保健疾病检测和分类

Heba G Mohamed¹, Fadwa Alrowais², Fahd N Al-Wesabi³

¹Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia. hegmohamed@pnu.edu.sa.

Scientific reports

|July 2, 2025

概括

一种用于医疗保健疾病检测和分类 (MCODBC-HDDC) 的新型改进的Coati优化驱动区块链方法提高了疾病诊断的准确性. 这种人工智能方法实现了97.36%的准确性,改善了患者监测和数据安全性.

科学领域:

医疗保健信息学医疗保健信息学
人工智能在医学中的应用
医疗保健中的数据安全性

背景情况:

越来越多的患者数量和复杂的疾病挑战了传统的健康监测.
管理大量的异质健康数据和确保患者隐私是关键问题.
早期疾病预测和准确的分类对于有效的医疗保健至关重要.

研究的目的:

为医疗保健提出一种高效准确的疾病检测和分类方法.
为了提高诊断能力,利用深度学习技术.
通过区块链技术加强医疗保健中的数据安全和管理.

主要方法:

医疗保健疾病检测和分类 (MCODBC-HDDC) 改进的Coati优化驱动区块链方法集成区块链来保护数据.
数据预处理包括Z分数规范化,并使用SHOA进行特征选择.
疾病的检测和分类使用了注意力双向门循环单元 (ABiGRU),该单元由修改的科蒂优化算法 (MCOA) 优化.

主要成果:

在疾病检测和分类方面,MCODBC-HDDC方法表现出卓越的性能.
对HD数据集的实验分析得出了97.36%的准确性.
该方法为患者数据管理提供了一个安全,分散和防改的环境.

结论:

MCODBC-HDDC方法在人工智能驱动的医疗保健诊断方面取得了重大进展.
区块链和深度学习的整合提高了数据的准确性和安全性.
这种方法有望通过早期和精确的疾病识别来改善患者的治疗结果.

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