计算机与现代化 ›› 2020, Vol. 0 ›› Issue (12): 61-66.

• 数据库与数据挖掘 • 上一篇    下一篇

利用机器学习方法对灾难生命支持课程NDLS培训效果进行分析预测#br#

  

  1. (1.上海杉达学院信息科学与技术学院大数据分析与处理研究中心,上海201209;
    2.上海交通大学医学院附属新华医院,上海200092)
  • 出版日期:2021-01-07 发布日期:2021-01-07
  • 作者简介:郭欣(1982—),女,河南新乡人,讲师,硕士,研究方向:数据挖掘与分析,计算机应用,E-mail: guoxin@sandau.edu.cn; 通信作者:汤璐佳(1982—),女,主治医师,硕士,研究方向:急诊医学,灾难医学,E-mail: tanglujia@xinhuamed.com.cn。
  • 基金资助:
    教育部高教司协同育人项目(201901283035, 201901283036); 上海杉达学院校级重点教学改革项目(A020203.19.013)

Analysis and Prediction of Training Effects of National Disaster Life Support Course with Machine Learning Methods

  1. (1. Research Center of Big Data Analysis and Process, School of Information Science and Technology, 
    Shanghai Sanda University, Shanghai 201209, China;
    2. Xinhua Hospital Affiliated to School of Medicine of Shanghai Jiao Tong University, Shanghai 200092, China)
  • Online:2021-01-07 Published:2021-01-07

摘要: 2019年底新型冠状病毒(2019-nCoV)肺炎疫情爆发,疫情使人们更加认识到应急救援队伍建设和相关人才培养的重要性。本文以2018—2019年间参加灾难生命支持课程(National Disaster Life Support, NDLS)培训的学员数据为研究对象,利用非监督和监督2种机器学习方法相结合的方式,分析NDLS培训效果的影响因素,帮助培训组织对学员的培训效果进行有效预判,从而提前干预,提高培训质量。首先运用Apriori算法找出若干个对培训效果影响较大的因素,然后用决策树模型对培训效果进行预测,并利用决策树分析的结果验证关联分析的结论。以置信度、支持度及提升度等参数作为Apriori关联规则的评价指标。用十折交叉验证作为决策树预测模型评估的方法。结果显示模型效果良好,其结果可以帮助培训组织对学员的学习效果进行有效预判、监控并保证培训质量。

关键词: 机器学习, 灾难生命支持课程, 影响因素, Apriori关联规则分析, 决策树

Abstract: The outbreak of novel coronavirus (2019-nCoV) pneumonia occurred at the end of 2019. The epidemic situation makes us more aware of the importance of emergency rescue team construction and related personnel training. On the basis of participants in national disaster life support (NDLS) training data from 2018 to 2019, this paper is to study influence factors of NDLS training effects with machine learning method with a view to assisting organizations in evaluating the training effects, using Apriori and decision tree algorithm to build the model. Through data preprocessing, 14 fields are selected as model input fields, and test scores are selected as model output field. Firstly, Apriori algorithm is used to find out several factors that have a great influence on the training effect. Then the decision tree model is used to predict the training effects, and the results of decision tree are used to verify the conclusion of Apriori algorithm. The parameters of confidence, support and promotion are used as the evaluation indexes of Apriori algorithm. Ten fold cross validation is used as the evaluation method of decision tree model. The results show that the model is effective. Conclusion of this study can help the training organizations to effectively predict the learning effects of the trainees; in addition, it can monitor and ensure the quality of training.

Key words: machine learning, national disaster life support (NDLS), influence factors, Apriori algorithm, decision tree