Volume 16, Issue 54 (1-2014)                   jha 2014, 16(54): 34-45 | Back to browse issues page

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Azizi A, Zarei J, Nabovati E, Vakili-Arki H, Abbasi E, Razavi A. Determining of the Factors Affecting Mortality in Burn Patients Using a Decision Tree Data Mining Algorithm . jha. 2014; 16 (54) :34-45
URL: http://jha.iums.ac.ir/article-1-1317-en.html
1- PhD Student of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
2- PhD Student of Health Information Management, Health Management and Economics Research Center, School of health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
3- Instructor of Health Information Management and Technology Department, Kashan University of Medical Sciences, Kashan, Iran
4- PhD Student of Medical Informatics, Member of Cancer Informatics Research Group, Breast Cancer Research Center, ACECR, Tehran, Iran
5- Assistant Professor of Medical Informatics Department, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran , razaviar@mums.ac.ir
Abstract:   (5449 Views)


 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.

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Type of Study: Research | Subject: General
Received: 2013/05/8 | Accepted: 2013/10/19 | Published: 2013/11/20

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