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

Touraj Ahmadi-Jouybari,Khadijeh Najafi-Ghobadi,,Reza Karami-Matin,Saeid Najafian-Ghobadi,Somayeh Najafi-Ghobadi

doi:10.1186/s12874-021-01270-5

Abstract

BackgroundBurn 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.MethodsThis 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).ResultsThe 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.ConclusionsThe 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.

Highlights

Burn is a tragic event for an individual, the family, and community
The specificity of the Support Vector Machine (SVM) model and Random Forest (RF) model was higher than other models, and the mean negative likelihood ratio of the SVM model are higher than other models
In the Decision Tree (DT) model, indicators such as total accuracy, sensitivity, positive likelihood ratio, negative predictive value and positive predictive value were higher than other models

Summary

Introduction

Burn is a tragic event for an individual, the family, and community It can cause irreparable physical, mental, economic, and social injury. The burn is a tragic event for individuals, families, and communities It can cause irreparable physical, psychological, economic, and social injury. Burns are the third leading cause of death in the United States after accident and drowning, and the sixth leading cause of death in Iran [5]. It is a well-known fact that the most important factors in the mortality of burn patients are age, inhalation of burns and percentage of the total body surface area (TBSA) [6, 7]

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