生物灵感混合优化和深信神经网络用于早期慢性病检测:一个可解释的临床AI框架
Zohaib Ahmad1, Waeal J Obidallah2, Muhammad Shafiq3
1Department of Criminology, Lahore Garrison University, Lahore, Pakistan.
Renal failure
|March 16, 2026
概括
本研究引入了一种自动化算法,用于使用混合优化策略和深度学习检测慢性病 (CKD). 新的框架实现了高准确性,并确定了早期CKD查的关键临床特征.
科学领域:
- 科和人工智能的人工智能
- 计算生物学和生物信息学
背景情况:
- 早期发现慢性病 (CKD) 对于及时干预至关重要,但当前的诊断系统面临冗余特征,不平衡数据集的不可靠结果和缺乏透明度的困难.
- 现有的CKD诊断工具通常需要大量的手工劳动来微调,这限制了它们的临床适用性.
研究的目的:
- 开发一个完全自动化的,可解释的,计算效率高的CKD查管道.
- 通过优化功能选择和模型参数调整,提高CKD检测系统的性能和可靠性.
主要方法:
- 结合基于混合螺旋搜索策略的二元引力搜索算法 (SSS-BGSA) 和大象群体优化 (EHO) 的算法被开发来优化深信神经网络 (DBNN).
- 在特征选择中,SSS-BGSA被用来改善勘探-开发平衡,而EHO被用来提高DBNN的融合率和学习效率.
- 该框架在UCI CKD数据集上使用嵌套分层5倍交叉验证进行了验证.
主要成果:
- 该SSS-BGSA-EHO-DBNN框架实现了0.973 ± 0.022的高精度和0.996 ± 0.006.6的AUC.
- 该模型成功地确定了至少7个临床显著特征,包括特异重量,高血压,细胞密集体积,葡萄糖尿和血尿素.
- 拟议的方法表现出具有竞争力的性能,表明其对有效CKD查的潜力.
结论:
- 双阶段优化与可解释的深度学习相结合,为CKD查提供了一个全自动化,可解释和高效的管道.
- 开发的框架通过自动化功能选择和模型优化来解决现有系统的局限性,从而提高诊断性能.
- 确定关键的临床特征强调了该模型能够为早期CKD检测提供临床相关的见解的能力.
相关概念视频
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01:18Chronic Kidney Disease IV: Nursing Management
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01:28Drug Dosing in Renal Diseases: Estimation of Glomerular Filtration Rate Based on Serum Creatinine Concentration
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