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A comprehensive health classification model based on support vector machine for proseal laryngeal mask and tracheal catheter assessment in herniorrhaphy.
Mathematical Biosciences and Engineering ( IF 2.6 ) Pub Date : 2019-12-18 , DOI: 10.3934/mbe.2020097
Zhen Shuang Du 1 , Qing Wei Yang 2 , He Fan He 1 , Ming Xia Qiu 3 , Zhi Yao Chen 1 , Qing Fu Hu 1 , Qing Mao Wang 2 , Zi Ping Zhang 1 , Qiong Hua Lin 1 , Liu Yue Huang 1 , Ya Jiao Huang 1
Affiliation  

Purpose: In order to classify different types of health data collected in clinical practice of hernia surgery more effectively and improve the classification performance of support vector machine (SVM). Methods: A prospective randomized study was conducted. Sixty patients undergoing hernia repair under general anesthesia were randomly divided into two groups, PLMA group (n = 30) and ETT group (n = 30), for airway management. Heart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, respiratory parameters and the incidence of complications related to ProSeal laryngeal mask airway (PLMA) and endotracheal tube (ETT) were collected in clinical experiments in order to evaluate the operation condition. On the basis of this experiment, at first, expert credibility is introduced to process the index value; secondly, the classification weight of the index is objectively determined by the information entropy output of the index itself; finally, a comprehensive classification model of support vector machine based on key sample set is proposed and its advantages are evaluated. Result: After classifying the experimental data, we found that SVM can accurately judge the effect of surgery by data. In this experiment, PLMA method is better than ETT method in xenon repair operation. Discussion: SVM has great accuracy and practicability in judging the outcome of xenon repair operation. Conclusion: The proposed index classification weight model can deal with the uncertainties caused by uncertain information and give the confidence of the uncertain information. Compared with the traditional SVM method, the proposed method based on SVM and key sample set greatly reduces the number of samples that misjudge the effect of samples, and improves the practicability of SVM method. It is concluded that PLMA is superior to the ETT technique to hernia surgical. The idea of constructing classification model based on key sample set proposed in this paper can also be used for reference in other data mining methods.

中文翻译:

一种基于支持向量机的综合健康分类模型,用于疝修补术中的proseal喉罩和气管导管评估。

目的:为了更有效地对临床疝气手术中收集到的不同类型的健康数据进行分类,提高支持向量机(SVM)的分类性能。方法:进行了一项前瞻性随机研究。将60例全身麻醉下疝修补术患者随机分为两组,PLMA组(n=30)和ETT组(n=30)进行气道管理。在临床实验中收集心率、收缩压、舒张压、平均动脉压、呼吸参数以及ProSeal喉罩气道(PLMA)和气管导管(ETT)相关并发症的发生率,以评估手术情况。在本实验的基础上,首先引入专家可信度对指标值进行处理;第二,指标的分类权重由指标本身的信息熵输出客观确定;最后,提出了一种基于关键样本集的支持向量机综合分类模型,并对其优势进行了评价。结果:对实验数据进行分类后,我们发现SVM可以通过数据准确判断手术效果。在本实验中,PLMA方法在氙气修复操作中优于ETT方法。讨论:SVM 在判断氙气修复手术结果方面具有很高的准确性和实用性。结论:所提出的指标分类权重模型能够处理不确定信息带来的不确定性,给出不确定信息的置信度。与传统的 SVM 方法相比,所提出的基于SVM和关键样本集的方法大大减少了误判样本效果的样本数量,提高了SVM方法的实用性。结论PLMA优于ETT技术进行疝气手术。本文提出的基于关键样本集构建分类模型的思想,也可以在其他数据挖掘方法中借鉴。
更新日期:2019-12-18
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