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

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.