Investigating factors affecting the interval between a burn and the start of treatment using data mining methods and logistic regression

• Published in: BMC medical research methodology 2021-04, Vol.21 (1), p.71-71, Article 71
• Main Authors: Ahmadi-Jouybari, Touraj, Najafi-Ghobadi, Somayeh, Karami-Matin, Reza, Najafian-Ghobadi, Saeid, Najafi-Ghobadi, Khadijeh
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Burn treatment; Burns; Burns and scalds; Care and treatment; Classification; Data mining; Decision tree; Education; Fatalities; Hospitals; Injuries; Logistic regression; Logistics; Medical research; Methods; Patients; Random Forest; Research methodology; Software; Start of treatment; Statistical analysis; Support Vector Machine; Variables
• ISSN: 1471-2288; 1471-2288
• 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.