摘要
血管学高速发展的背景下,有关颈动脉的自动化诊断获得社会各界广泛关注。但是常规方法在面对复杂环境时,会产生不可忽视的误差。为了解决这个问题,此次研究在极限梯度提升算法中加入特征集合方法,并使用权重因子优化数据挖掘技术,生成融合算法。并在该算法中添加二叉树规则,生成融合算法。最后,研究在Sclero数据集上进行实验,并与黄金正弦等三种系统进行比较。在一天内,融合系统的耗电量为0.21 kW*h,在四种系统中耗电最低。经过一个月的诊断,患者颈动脉的硬化评分分别为2.8、3.0、3.1和3.4,说明研究提出方法的治愈效果最好,且其血液流速评分为3.2,说明该方法对患者的适应度最高。实验结果表明,研究提出的融合系统在实验精度、患者颈动脉硬度诊断均获得最优效果,适于对颈动脉硬化患者进行诊断。
Against the backdrop of rapid development in vascular science,automated diagnosis of carotid arteries has gained widespread attention from all sectors of society.However,in the face of complex environment,conventional methods will produce errors that cannot be ignored.In order to solve this problem,the feature set method is added to the extreme gradient lifting algorithm,and the weight factor is used to optimize the data mining technology to generate a fusion algorithm.The binary tree rule is added to the algorithm to generate a fusion algorithm.Finally,the experiment is carried out on Sclero data set and compared with three systems,such as Golden Sine.In one day,the power consumption of the converged system is 0.21 kW*h,which is the lowest among the four systems.After one month’s diagnosis,the patients’carotid atherosclerosis scores were 2.8,3.0,3.1 and 3.4,respectively,which indicated that the proposed method had the best curative effect,and its blood flow rate score was 3.2,which indicated that the method had the highest adaptability to patients.The experimental results show that the fusion system proposed in this study has achieved the best results in experimental accuracy and diagnosis of patients’carotid hardness,and is suitable for diagnosis of patients with carotid atherosclerosis.
作者
董雪
DONG Xue(Baoji University of Arts and Sciences,Baoji Shanxi 721000,China)
出处
《自动化与仪器仪表》
2024年第4期230-234,共5页
Automation & Instrumentation
基金
陕西省教育厅自然科学专项(21JK0473)。
关键词
数据挖掘
权重因子
极限梯度提升算法
特征集合
二叉树规则
颈动脉硬化
自动诊断
data mining
weight factor
limit gradient lifting algorithm
feature set
bifurcation tree rule
carotid atherosclerosis
automatic diagnosis
