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使用增强的狮优化支向量机器预测分数流量储备

Haoxuan Lu¹, Li Huang², Yanqing Xie¹

¹Department of Cardiology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, PR China.

|August 17, 2023

概括

一个新的混沌高斯变异线优化算法 (CGALO) 提高了分量流量储备 (FFR) 预测的准确性. 这种人工智能模型通过识别关键指标,协助医生诊断冠心病.

科学领域:

心脏病学心脏病学
人工智能的人工智能
计算优化计算优化

背景情况:

冠状动脉疾病 (CHD) 的诊断依赖于评估冠状动脉形态.
分流储备 (FFR) 是评估冠状动脉狭窄症生理意义的关键指标.
现有的优化算法对FFR预测模型的速度和准确性有局限性.

研究的目的:

为了介绍一个新的混沌高斯变异Antlion优化算法 (CGALO).
开发和验证使用CGALO和支持矢量机器 (SVM) 的FFR改进的分类预测模型.
确定影响冠状动脉疾病患者FFR值的关键指标.

主要方法:

拟议的CGALO算法结合了一个混乱的高斯突变策略来增强原来的Antlion Optimizer (ALO).
对23个基准函数进行了比较实验,对比了12个最先进的优化算法.
通过将CGALO与SVM和特征选择 (FS) 集成,构建了一个层次的FFR分类模型,并应用于预测84名患者的FFR.

主要成果:

在基准测试中,CGALO与其他12个领先的优化算法相比,显示出更高的融合速度和准确性.
开发的CGALO-SVM-FS模型在临床患者队列中实现了FFR的平均预测准确率92%的FFR.
确定了FFR预测的关键指标,包括吸烟史,患病血管数量,病变位置,扩散病变和ST段变化.

结论:

CGALO算法在优化任务的收和准确性方面提供了显著的改进.
拟议的智能分类预测模型为协助临床医生在FFR评估和决策方面提供了有效的工具.
这种由人工智能驱动的方法代表了一种用于提高冠心病诊断和管理的新技术.

关键词:

狮子优化器狮子优化器冠心病是一种冠心病.功能选择功能选择分量流量储备部分流量储备全球优化全球优化支持矢量机器的支持矢量机器.

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