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:: Volume 25, Issue 1 (5-2017) ::
Journal of Ilam University of Medical Sciences 2017, 25(1): 20-32 Back to browse issues page
Heart disease forecast using neural network data mining technique
Maryam Kazemi1 , Hossein Mehdizadeh * 2, Ardeshir Shiri3
1- University of Ilam medical sciences
2- Ilam University , Hossein.mahdizadeh@gmail.com
3- Dept of Management, Faculty of Humanities, Ilam University,Ilam, Iran
Abstract:   (9606 Views)




Data mining refers to the study and analysis of large amounts of data for discovering meaningful patterns and rules. Mainly through the models and algorithms, data mining puts the inputs in a specific order. Data mining techniques sometimes lead to the identification of meaningful algorithms which can use available and low-cost data in order to provide us with areas of infection, prevention, and treatment of diseases and help the physicians in timely and accurate diagnosis.

Materials & Methods

The present paper aimed to study the use of the results of data mining of hospital information systems by hospital managers for more accurate prediction and more effective decision-making about treatment of patients. The data used in this study included the information of 270 patients (14 variables) extracted from the database of UCI website. A “neural networks” model was used for the prediction of affliction with heart disease and its accuracy was measured and compared.

Research findings

According to the results, it can be observed that Multilayer Perceptron Neural Networks Model has classified the set of test observations with an accuracy of 83.33%.

Discussion & Conclusion

The results showed that the accuracy of “neural networks” model in classification of records in terms of heart disease response is 87.75% for the set of modeling records and 83.33% for the set of test records. In addition, the findings revealed that the variables of the number of large vessels (Nbr-ves), stress reduction (ST-dep), defect, chest pain, stress peak (Peak-ST), heart rate, angina, gender, age, static ECG (Res-elec), blood pressure (Blood-press), blood sugar, and serum cholesterol (Serum-chol), respectively, have the highest importance in “Multilayer Perceptron Neural Networks” model for the prediction of heart disease response.   

Keywords: Data mining, Neural network, Heart-disease
Full-Text [PDF 669 kb]   (5541 Downloads)    
Type of Study: Research |
Received: 2014/12/15 | Accepted: 2016/02/27 | Published: 2017/05/1
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Kazemi M, Mehdizadeh H, Shiri A. Heart disease forecast using neural network data mining technique. J. Ilam Uni. Med. Sci. 2017; 25 (1) :20-32
URL: http://sjimu.medilam.ac.ir/article-1-2376-en.html

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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 25, Issue 1 (5-2017) Back to browse issues page
مجله دانشگاه علوم پزشکی ایلام Journal of Ilam University of Medical Sciences
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