A comprehensive health classification model based on support vector machine for proseal laryngeal mask and tracheal catheter assessment in herniorrhaphy

Zhenshuang Du; Qingwei Yang; Hefan He; Mingxia Qiu; Zhiyao Chen; Qingfu Hu; Qingmao Wang; Ziping Zhang; Qionghua Lin; Liuyue Huang; Yajiao Huang; Zhenshuang Du; Qingwei Yang; Hefan He; Mingxia Qiu; Zhiyao Chen; Qingfu Hu; Qingmao Wang; Ziping Zhang; Qionghua Lin; Liuyue Huang; Yajiao Huang

doi:10.3934/mbe.2020097

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 2: 1838-1854. doi: 10.3934/mbe.2020097

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A comprehensive health classification model based on support vector machine for proseal laryngeal mask and tracheal catheter assessment in herniorrhaphy

1.
General Practice, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, Fujian, China
2.
Department of General Surgery, Shishi City Hospital, Shishi 362700, Fujian, China
3.
Hubin Street Health Service Centre of Shishi, Shishi 360007, Fujian, China

Received: 19 March 2019 Accepted: 17 July 2019 Published: 18 December 2019

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.
- support vector machine,
- index classification weight,
- information entropy,
- data mining,
- comprehensive classification model
Citation: Zhenshuang Du, Qingwei Yang, Hefan He, Mingxia Qiu, Zhiyao Chen, Qingfu Hu, Qingmao Wang, Ziping Zhang, Qionghua Lin, Liuyue Huang, Yajiao Huang. A comprehensive health classification model based on support vector machine for proseal laryngeal mask and tracheal catheter assessment in herniorrhaphy[J]. Mathematical Biosciences and Engineering, 2020, 17(2): 1838-1854. doi: 10.3934/mbe.2020097

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Abstract

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.

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