Investigating factors affecting the interval between a burn and the start of treatment using data mining methods and logistic regression
| Autor: | Touraj Ahmadi-Jouybari, Khadijeh Najafi-Ghobadi, Saeid Najafian-Ghobadi, Reza Karami-Matin, Somayeh Najafi-Ghobadi |
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| Rok vydání: | 2021 |
| Předmět: |
Medicine (General)
Support Vector Machine Epidemiology Hospitalized patients Decision tree Logistic regression Health Informatics Interval (mathematics) computer.software_genre Likelihood ratios in diagnostic testing 03 medical and health sciences R5-920 0302 clinical medicine Data Mining Humans Medicine 030212 general & internal medicine Random Forest Data collection business.industry 030208 emergency & critical care medicine Random forest Support vector machine Start of treatment Cross-Sectional Studies Logistic Models Data mining Burns business computer Research Article |
| Zdroj: | BMC Medical Research Methodology, Vol 21, Iss 1, Pp 1-6 (2021) BMC Medical Research Methodology |
| ISSN: | 1471-2288 |
| Popis: | Background 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. Methods 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). Results 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. Conclusions 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. |
| Databáze: | OpenAIRE |
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