Determining of the Factors Affecting Mortality in Burn Patients Using a Decision Tree Data Mining Algorithm

• Main Authors: AA Azizi, J Zarei, E Nabovati, H Vakili-Arki, E Abbasi, AR Razavi
• Format: Article
• Language: fas
• Published: Iran University of Medical Sciences 2014-01-01
• Series: مدیریت سلامت
• Subjects: Data Mining; Decision Tree; Burn
• Online Access: http://jha.iums.ac.ir/article-1-1317-en.html
• ISSN: 2008-1200; 2008-1219

Introduction : Using data mining to develop prediction models from large amounts of data in medicine has become increasingly popular during the recent years. In this study, we intend to use a decision tree data mining algorithm in order to identify factors influencing mortality in burn patients.   Methods: The present retrospective descriptive study is based on burning patients’ records. Overall, the medical records of 4804 patients were scrutinized. The collected data were analyzed using statistical software (SPSS version 16), data mining software (Clementine version 12), and CHAID algorithm.   Results: The resulting model for predicting survival and mortality of burning patients included the percentage of Total Burn Surface Area (TBSA %), degree of burn, length of stay, gender, and age of patients. Other variables including blood cultures, wound cultures, urine cultures, and the months of patient hospitalization had no effect on improving the efficiency of the model.   Conclusion: The proposed model is valid and reliable due to its accuracy (approximately 95%). In fact, the results of this study, some of which are consistent with the results of other studies, can propose a comprehensive, accurate, and reliable local model for predicting mortality and survival of burning patients based on the mentioned variables. Thus, this local model can be used as an important criterion for evaluating the effective treatment of burn patients.