Myocardial Infarction Prediction and Estimating the Importance of its Risk Factors Using Prediction Models

Fatemeh Rahimi, Mahdi Nasiri, Reza Safdari, Goli Arji, Zahra Hashemi, Roxana Sharifian


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.


Cardiovascular disease; data mining; myocardial infarction; risk factor

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World Health Organization. Fact sheet 317: cardiovascular

disease. [Last updated on 2021 june]. Available from: https:// [Last accessed on 2022 Nov 04].

Mensah GA, Roth GA, Fuster V. The Global burden of

cardiovascular diseases and risk factors: 2020 and beyond. J Am

Coll Cardiol 2019;74:2529–32.

Soni J, Ansari U, Sharma D, Soni S. Predictive data mining for

medical diagnosis: An overview of heart disease prediction. Int J

Comput Appl 2011;17:43–8.

Fact sheet: Risk factors for cardiovascular disease (CVD):

HEART UK. The Cholesterol charity. Available from: https:// [Last accessed on 2020 Aug 09].

World Health Organization. Global atlas on cardiovascular

disease prevention and control. Edited by Mendis et al. 2011.

Cardiovascular disease risk factors. World Heart Federation;

Available from:‑heart‑

[Last accessed on 2020 Aug 09].

Moses A, Sathishkumar R, Meghana M, Meghana Raju M,

Madhumitha M. Forecasting myocardial infarction using machine

learning algorithms. Int J Pure Appl Math 2018;118:859–63.

Mukherjee S, Kapoor S, Banerjee P. Diagnosis and identification

of risk factors for heart disease patients using generalized

additive model and data mining techniques. J Cardiovasc Dis

Res 2017;8:137‑44.

Seenivasagam V, Chitra R. Myocardial infarction detection using

intelligent algorithms. Neural Netw World 2016;26:91.

Dangare C, Apte S. A data mining approach for prediction of

heart disease using neural networks. Int J Comput Eng Technol.


Rajkumar A, Reena GS. Diagnosis of heart disease using

datamining algorithm. Glob J Comput Sci Technol 2010;10:38–43.

Patil SB, Kumaraswamy YS. Extraction of significant patterns

from heart disease warehouses for heart attack prediction.

IJCSNS. 2009;9:228–35.

Amin SU, Agarwal K, Beg R. Genetic neural network based

data mining in prediction of heart disease using risk factors.

In: 2013 IEEE Conference on Information & Communication

Technologies. IEEE; 2013. p. 1227–31.

Subbalakshmi G, Ramesh K, Rao MC. Decision support in heart

disease prediction system using naive bayes. Indian J Comput

Sci Eng 2011;2:170–6.

Dangare CS, Apte SS. Improved study of heart disease prediction

system using data mining classification techniques. Int J Comput

Appl 2012;47:44–8.

Tomar D, Agarwal S. A survey on data mining approaches for

healthcare. Int J Bio‑Sci Bio‑Technol 2013;5:241–66.

Sandmaier M. Your guide to healthy heart. U.S. Department

of Health and Human Servies, National Institutes of Health, &

National Heart, Lung, and Blood Institute 2005. Available from: [Last accessed on 2022 Dec 14].

The healthy heart handbook for woman. U.S. Department of

Health and Human Services, National Institutes of Health,

National Heart, Lung, and Blood Institute 2007. Available from: [Last accessed on 2022 Dec 14].

Prevention of Cardiovascular Disease, Guidelines for assessment

and management of cardiovascular risk. World Health

Organization; 2007.

Berner ES, editor. Clinical Decision Support Systems: Theory and

Practice. Switzerland: Springer International Publishing; 2016.

Uddin S, Khan A, Hossain M, Moni MA. Comparing different

supervised machine learning algorithms for disease prediction.

BMC Med Inform Decis Mak 2019;19:281.

Safdari R, Gharooni M, Nasiri M, Argi G. Comparing

performance of decision tree and neural network in predicting

myocardial infarction. J Paramed Sci Rehabil 2014;3:26–35.

The UCI machine learning repository [Data base]. Available

from: [Last accessed on 2022 Dec 14].

Venkatalakshmi B, Shivsankar MV. Heart disease diagnosis

using predictive data mining. Int J Innov Res Sci Eng Technol 2014;3:1873–7.

Palaniappan S, Awang R. Intelligent heart disease prediction

system using data mining techniques. In: 2008 IEEE/ACS

international conference on computer systems and applications.

IEEE; 2008. p. 108–15.

Karaolis MA, Moutiris JA, Hadjipanayi D, Pattichis CS. Assessment

of the risk factors of coronary heart events based on data mining with

decision trees. IEEE Trans Inf Technol Biomed 2010;14:559–66.

Ahmed A, Hannan SA. Data mining techniques to find out

heart diseases: An overview. Int J Innov Technol Explor Eng


Zarrabi M, Parsaei H, Boostani R, Zare A, Dorfeshan Z,

Zarrabi K, et al. A system for accurately predicting the risk of

myocardial infarction using PCG, ECG and clinical features.

Biomed Eng Appl Basis Commun. 2017;29:1750023.

Korley FK, Gatsonis C, Snyder BS, George RT, Abd T,

Zimmerman SL, et al. Clinical risk factors alone are inadequate

for predicting significant coronary artery disease. J Cardiovasc

Comput Tomogr 2017;11:309–16.

Eyre H, Kahn R, Robertson RM, Committee AC, Members AC,

Clark NG, et al. Preventing cancer, cardiovascular disease, and

diabetes: A common agenda for the American Cancer Society,

the American Diabetes Association, and the American Heart

Association. Circulation 2004;109:3244–55.

Karimi S, Javadi M, Jafarzadeh F. Economic burden and costs

of chronic diseases in Iran and the world. Health Inf Manag


Sharifian R, SedaghatNia MH, Nematolahi M, Zare N,

Barzegari S. Estimation of completeness of cancer registration

for patients referred to shiraz selected centers through a two

source capture re‑capture method, 2009 data. Asian Pacific J

Cancer Prev 2015;16:5549–56.