A comprehensive health classification model based on support vector machine for proseal laryngeal mask and tracheal catheter assessment in herniorrhaphy
| Autor: | Zhen Shuang Du, Qing Wei Yang, Liu Yue Huang, Qing Mao Wang, Qing Fu Hu, He Fan He, Zi Ping Zhang, Ya Jiao Huang, Qiong Hua Lin, Zhi Yao Chen, Ming Xia Qiu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Catheters
Support Vector Machine Computer science medicine.medical_treatment Sample (statistics) 02 engineering and technology Laryngeal Masks Set (abstract data type) Laryngeal mask airway comprehensive classification model 0502 economics and business 0202 electrical engineering electronic engineering information engineering medicine QA1-939 Humans Hernia Prospective Studies Herniorrhaphy business.industry Applied Mathematics 05 social sciences Experimental data information entropy Pattern recognition General Medicine data mining medicine.disease Hernia repair Support vector machine Computational Mathematics Modeling and Simulation 020201 artificial intelligence & image processing Airway management index classification weight Artificial intelligence General Agricultural and Biological Sciences business 050203 business & management TP248.13-248.65 Mathematics Biotechnology |
| Zdroj: | Mathematical Biosciences and Engineering, Vol 17, Iss 2, Pp 1838-1854 (2020) |
| ISSN: | 1551-0018 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|