Clinical Research Development Center, Imam Khomeini and Mohammad Kermanshahi Hospitals, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Department of Industrial Engineering, Faculty of Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
BMC Med Res Methodol. 2021 Apr 14;21(1):71. doi: 10.1186/s12874-021-01270-5.
Burn is a tragic event for an individual, the family, and community. It can cause irreparable physical, mental, economic, and social injury. Researches well documented that a quick visit to a healthcare center can greatly reduce burn injuries. Therefore, the aim of this study is to identify the effective factors in the interval between a burn and start of treatment in burn patients by comparing three classification data mining methods and logistic regression.
This cross-sectional study conducted on 389 hospitalized patients in Imam Khomeini Hospital of Kermanshah city since 2012 to 2015. The data collection instrument was a three-part questionnaire, including demographic information, geographical information, and burn information. Four classification methods (decision tree (DT), random forest (RF), support vector machine (SVM) and logistic regression (LR)) were used to identify the effective factors in the interval between burn and start of treatment (less than two hours and equal or more than two hours).
The mean total accuracy of all models is higher than 0.8. The DT model has the highest mean total accuracy (0.87), sensitivity (0.44), positive likelihood ratio (14.58), negative predictive value (0.89) and positive predictive value (0.71). However, the specificity of the SVM model and RF model (0.99) was higher than other models, and the mean negative likelihood ratio (0.98) of the SVM model are higher than other models.
The results of this study shows that DT model performed better that data mining models in terms of total accuracy, sensitivity, positive likelihood ratio, negative predictive value and positive predictive value. Therefore, this method is a promising classifier for investigating the factors affecting the interval between a burn and the start of treatment in burn patients. Also, key factors based on DT model were location of transfer to hospital, place of occurrence, time of accident, religion, history and degree of burn, income, province of residence, burnt limbs and education.
烧伤对个人、家庭和社区来说都是一件悲惨的事情。它会造成不可挽回的身体、心理、经济和社会伤害。研究表明，迅速前往医疗中心就诊可以大大降低烧伤的严重程度。因此，本研究的目的是通过比较三种分类数据挖掘方法和逻辑回归，确定烧伤患者从烧伤到开始治疗的时间间隔的影响因素。
这是一项横断面研究，于 2012 年至 2015 年期间在克尔曼沙阿市伊玛目霍梅尼医院对 389 名住院患者进行。数据收集工具是一个三部分的问卷，包括人口统计学信息、地理位置信息和烧伤信息。使用四种分类方法（决策树（DT）、随机森林（RF）、支持向量机（SVM）和逻辑回归（LR））来识别烧伤到开始治疗的时间间隔（小于 2 小时和等于或大于 2 小时）的影响因素。
所有模型的平均总准确率均高于 0.8。DT 模型的平均总准确率（0.87）、灵敏度（0.44）、阳性似然比（14.58）、阴性预测值（0.89）和阳性预测值（0.71）最高。然而，SVM 模型和 RF 模型的特异性（0.99）高于其他模型，SVM 模型的平均阴性似然比（0.98）也高于其他模型。
本研究结果表明，DT 模型在总准确率、灵敏度、阳性似然比、阴性预测值和阳性预测值方面优于数据挖掘模型。因此，该方法是一种有前途的分类器，可用于研究影响烧伤患者从烧伤到开始治疗时间间隔的因素。此外，基于 DT 模型的关键因素包括转诊医院的位置、发生地点、事故发生时间、宗教、烧伤病史和程度、收入、居住地省份、烧伤部位和教育程度。
