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In medical science, it is very important to gather multiple data on differentdiseases and one of the most important objectives of the data is to investigatethe diseases. Myocardial infarction is a serious risk factor in mortality andin previous studies, the main emphasis has been on people with heart diseaseand measuring the likelihood of myocardial infarction in them throughdemographic features, echocardiography, and electrocardiogram. In contrast, thepurpose of the present study is to utilize data analysis algorithms and comparetheir accuracy in patients with a heart attack in order to identify the heartmuscle strength during myocardial infarction by taking into account emergencyoperations and consequently predict myocardial infarction. For this purpose,105 medical records of myocardial infarction patients with fourteen featuresincluding age, the time of emergency operation, Creatine Phosphokinase (CPK)test, heart rate, blood sugar, and vein are gathered and investigated throughclassification techniques of data analysis including random decision forests,decision tree, support vector machine (SVM), k-nearest neighbor, and ordinallogistic regression. Finally, the model of random decision forests with anaccuracy of 76% is selected as the best model in terms of the mean evaluationindicator. Also, seven features of the creatine Phosphokinase test, urea, whiteand red blood cell count, blood sugar, time, and hemoglobin are identified asthe most effective features of the ejection fraction variable.

Investigating myocardial infarction and its effects in patients with urgent medical problems using advanced data mining tools