Comparing performance of decision tree and neural network in predicting myocardial infarction

Safdari, R and Ghazi saeedi, M and Gharooni, M and Nasiri, M and Argi, G (2014) Comparing performance of decision tree and neural network in predicting myocardial infarction. Journal of Paramedical Sciences & Rehabilitation, 3 (2). pp. 26-35.

JPSR_Volume 3_Issue 2_Pages 26-35.pdf

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Purpose: Cardiovascular diseases are among the most common diseases in all societies. Using data mining techniques to generate predictive models to identify those at risk for reducing the effects of the disease is very helpful. The main purpose of this study was to predict the risk of myocardial infarction by Decision Tree based on the observed risk factors. Methods: The present work was an analytical study conducted on a database containing 350 records. Data were obtained from patients admitted to Shahid Rajaei specialized cardiovascular hospital, Iran, in 2011. Data were collected using a four-sectioned data collection form. Data analysis was performed using SPSS statistical software version 12 by CRISP methodology. In the modeling section decision tree and Neural Network were used. Results: The results of the data mining showed that the variables of high blood pressure, hyperlipidemia and tobacco smoking were the most critical risk factors of myocardial infarction. The accuracy of the decision tree model on the data was shown to be as 93/4. Conclusion: The best created model was decision tree C5.0. According to the created rules, it can be predicted which patient with new specified features may affected by myocardial infarction.

Item Type: Article
Subjects: WG Cardiovascular System
Divisions: Journals > Journal of Paramedical Science and Rehabilitation
Depositing User: jpsr jpsr
Date Deposited: 28 Sep 2017 16:07
Last Modified: 28 Sep 2017 16:07

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