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Raeesi, Ahmad, Ebrahimi, Saeed, Irfan Nia, Leila, Arji, Goli, Askani, Moslem. (1393). An investigation of data mining techniques of the performance of a decision tree algorithm for predicting causes of traumatic brain injuries in Khatamolanbya Hospital in Zahdan city, 2012 to 2013. سامانه مدیریت نشریات علمی, 1(2), 28-30.
Ahmad Raeesi; Saeed Ebrahimi; Leila Irfan Nia; Goli Arji; Moslem Askani. "An investigation of data mining techniques of the performance of a decision tree algorithm for predicting causes of traumatic brain injuries in Khatamolanbya Hospital in Zahdan city, 2012 to 2013". سامانه مدیریت نشریات علمی, 1, 2, 1393, 28-30.
Raeesi, Ahmad, Ebrahimi, Saeed, Irfan Nia, Leila, Arji, Goli, Askani, Moslem. (1393). 'An investigation of data mining techniques of the performance of a decision tree algorithm for predicting causes of traumatic brain injuries in Khatamolanbya Hospital in Zahdan city, 2012 to 2013', سامانه مدیریت نشریات علمی, 1(2), pp. 28-30.
Raeesi, Ahmad, Ebrahimi, Saeed, Irfan Nia, Leila, Arji, Goli, Askani, Moslem. An investigation of data mining techniques of the performance of a decision tree algorithm for predicting causes of traumatic brain injuries in Khatamolanbya Hospital in Zahdan city, 2012 to 2013. سامانه مدیریت نشریات علمی, 1393; 1(2): 28-30.

An investigation of data mining techniques of the performance of a decision tree algorithm for predicting causes of traumatic brain injuries in Khatamolanbya Hospital in Zahdan city, 2012 to 2013

Health Management & Information Science
مقاله 2، دوره 1، شماره 2، تیر 2014، صفحه 28-30    اصل مقاله (504.73 K)
نوع مقاله: Articles
نویسندگان
Ahmad Raeesi* ؛ Saeed Ebrahimi؛ Leila Irfan Nia؛ Goli Arji؛ Moslem Askani
چکیده
Introduction: The aim of this study was to determine the performance of data mining techniques for predicting the causes of traumaticbrain injuries in Khatamolanbya hospital, Zahdan city.Method: In this cross–sectional, the study population included all patients who died of brain injury. Data were collected by the use of aresearcher- made check list, provided under the direct observation of authorities in this area and analyzed by the data mining softwareof Clementine 12.0.Results: According to the results of this algorithm, C5.0 decision tree algorithm has an accuracy of 81.4 percent, the highest precision;then, the algorithm is C & R(The Classification and Regression) with 77.8 percent.Conclusion: Overall, it can be concluded from the decision tree algorithm that age is one of the leading causes of traumatic braininjuries . The results showed that all the cases involving traumatic lesions of the brain lead to the patient’s death.. Although in somealgorithms, some of the variables are important, they cannot be used alone as the main variable to be taken into account for the deathof the patient.Keywords: Data mining, Prediction of the factors of traumatic brain injuries
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