Journal of Paramedical Sciences & Rehabilitation

Journal of Paramedical Sciences & Rehabilitation

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

Document Type : Original Article

Authors
R Safdari 1
M Ghazi saeedi 2
M Gharooni 3
M Nasiri 4
G Argi 5
1 Associate Professor, Health Information Management Department, School of Allied Medicine, Tehran University of Medical Sciences, Tehran, Iran
2 Assistant Professor, Department of Health Information Management ,School of Allied Medicine , Tehran University Medical Sciences, Tehran, Iran
3 Professor of Cardiology, Tehran University of Medical Sciences, Tehran, Iran
4 PhD student of AI , Iran University of Science and Technology, Tehran, Iran
5 PhD student of Health Information Management, School of Allied Medicine, Tehran University of Medical Sciences, Tehran, Iran
10.22038/jpsr.2014.3319
Abstract
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.
Keywords
Decision Trees
Neural Network
Myocardial infarction
Data mining