使用XceptionNet与Harmonic Addax优化进行慢性病检测
Suruchi Gaurav Dedgaonkar1, Geeta S Navale1, Priya Shelke1
1Vishwakarma Institute of Technology, Pune, Maharashtra, 411037, India.
Computational biology and chemistry
|January 30, 2026
概括
这项研究引入了一个优化的深度学习框架,用于早期检测慢性病 (CKD). 新方法显著提高了诊断准确度,有助于预防功能衰竭.
科学领域:
- 科和人工智能的人工智能
- 医学成像和诊断 医学成像和诊断
- 计算健康科学 计算健康科学
背景情况:
- 慢性病 (CKD) 是一种进展性功能障碍,导致严重的健康并发症.
- 早期发现CKD对于预防功能衰竭和管理高血压和糖尿病等二次疾病至关重要.
- 现有的检测方法面临着普遍化和阶级不平衡的挑战,阻碍了准确的早期诊断.
研究的目的:
- 开发和验证一个优化的深度学习框架,以提高慢性病 (CKD) 的早期检测.
- 解决目前CKD检测方法的局限性,特别是泛化和类失衡问题.
- 提高CKD诊断的准确性和可靠性,以获得更好的患者结果.
主要方法:
- 提出了一个新的CKD检测框架,将XceptionNet与和优化算法 (HAOA) 集成在一起.
- 该框架采用了Sigmoid规范化,使用深度信念网络 (DBN) 与Soergel度量以及合成少数重叠技术 (SMOTE) 来增强数据的功能融合.
- 该HAOA算法是通过将波分析和Addax优化算法 (AOA) 结合而开发的.
主要成果:
- 拟议的Xception与HAOA模型在多个CKD数据集中表现出高性能.
- 实现了94.679%的真正阳性率 (TPR),92.777%的真负率 (TNR),整体准确率为93.667%.
- 报告了92.258%的精度和93.453%的F1得分,表明了强大的诊断能力.
结论:
- 开发的深度学习框架为早期CKD诊断提供了有效的解决方案.
- 整合XceptionNet和HAOA显著提高了检测准确性,克服了现有的局限性.
- 这种方法有可能通过及时干预来降低功能衰竭率并改善患者的预后.
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