alexa Heart Disease Diagnosis Using Data Mining Techniques
ISSN: 2162-6359

International Journal of Economics & Management Sciences
Open Access

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Research Article

Heart Disease Diagnosis Using Data Mining Techniques

Ramin Assari1*, Parham Azimi2 and Mohammad Reza Taghva1

1Department of IT Management, Allameh Tabataba’i University, Tehran, Iran

2Faculty of Mechanical Engineering and Industrial Engineering, Islamic Azad University, Qazvin, Iran

*Corresponding Author:
Ramin Assari
IT Management, Management Department
Allameh Tabataba’i University, Iran
Tel: 98 2144737510
E-mail: assari200020[email protected]

Received Date: December 13, 2016; Accepted Date: March 27, 2017; Published Date: March 29, 2017

Citation: Assari R, Azimi P, Taghva MR (2017) Heart Disease Diagnosis Using Data Mining Techniques. Int J Econ Manag Sci 6: 415. doi: 10.4172/2162-6359.1000415

Copyright: © 2017 Assari R, et al.. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.



In recent decades, heart disease has been identified as the leading cause of death across the world. However, it is considered as the most preventable and controllable disease at the same time. According to World Health Organization (WHO), the early and timely diagnosis of heart disease plays a remarkable role in preventing its progress and reducing related treatment costs. Considering the ever-increasing growth of heart disease-induced fatalities, researchers have adopted different data mining techniques to diagnose it. According to results, application of the same data mining techniques leads to different results in different datasets. This study tries to assist healthcare specialists to early diagnose heart disease and assess related risk factors. To this end, the main heart disease diagnosis indices were identified using experts’ opinions. Then, data mining techniques were applied on a heartrelated dataset. Finally, the main heart disease diagnosis indices were identified and a model was developed based on extracted rules. Visual Studio was used to write the algorithm code.


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