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Myocardial infarction prediction and estimating the importance of its risk factors using prediction models
Fatemeh Rahimi1, Mahdi Nasiri2, Reza Safdari3, Goli Arji3, Zahra Hashemi4, Roxana Sharifian5
1 Department of Health, Information Management, School of Management and Medical Information Sciences, Shiraz University of Medical Sciences, Shiraz, Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2 Department of Health, Information Management, School of Management and Medical Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
3 Department of Health Information Management, Tehran University of Medical Sciences, Tehran, Iran
4 Department of Pediatric Dentistry, School of Dentistry, Yasuj University of Medical Sciences, Yasuj, Iran
5 Department of Health Information Management, School of Management and Medical Information Sciences, Health Human Resources Center, Shiraz University of Medical Sciences, Shiraz, Iran
Correspondence Address:
Roxana Sharifian
School of Management and Medical Information Sciences, Qasroldasht 29st Alley, Qasroldasht Ave, Shiraz - 7133654361
Iran
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/ijpvm.IJPVM_504_20
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Background: According to World Health Organization (WHO), cardiovascular diseases (CVDs) are the leading cause of death globally. Although significant progress has been made in the diagnosis of CVDs, more investigation can be helpful. Therefore, this study aimed to predict the risk of myocardial infarction (MI) using data mining algorithms. Methods: The applied data were related to the admitted patients in Rajaei specialized cardiovascular hospital located in Tehran. At first, a literature review and interview with a cardiologist were conducted to understand MI. Then, data preparation (cleaning and normalizing the data) was performed. After all, different classification algorithms were applied in IBM SPSS Modeler (14.2) software on the prepared data; and, power of the applied algorithms and the importance of the risk factors in predicting the probability of getting involved with MI was calculated in the mentioned software. Results: This study was able to predict MI % 75.28 and 77.77% in terms of accuracy and sensitivity, respectively. The results also revealed that cigarette consumption, addiction, blood pressure, and cholesterol were the most important risk factors in predicting the probability of getting involved with MI, respectively. Conclusions: Predicting studies aim to support rather than replace clinical judgment. Our prediction models are not sufficiently accurate to supplant decision-making by physicians but have considerable tips about MI risk factors.
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