Death risk analysis and prediction of patients with ST-segment elevated myocardial infarction using artificial intelligence methods

Heart attack, acute myocardial infarction (AMI), is a condition in which a part of the heart muscle cannot receive enough blood due to the narrowing and blockage of the vessels feeding the heart over time. Noticing this situation late and failing to intervene immediately may cause death and some permanent damage for the individual. Looking at the statistics, deaths caused by cardiovascular diseases are increasing day by day. ST-segment elevation acute myocardial infarction (ST-segment Elevation Acute Myocardial Infarction - STEMI), one of the most serious and fatal types of acute myocardial infarction, requires urgent diagnosis and intervention. Artificial intelligence-based applications used in health have become widespread, paving the way for early diagnosis and treatment. In modern medicine, it is vital that STEMI patients are identified and treated accurately and quickly. In this process, determining the risk of death of patients in advance plays a major role in making clinical decisions. Traditional risk assessment methods are often time-consuming and subjective processes and rely on manual analysis of clinical data. In this study, death risk analysis and prediction of patients will be made using machine learning algorithms, which is one of the artificial intelligence methods. Based on the data set used, the parameters of those who had a heart attack, the distribution of these parameters on the data set and the situations in which they are related to each other will be examined with different analysis methods and it will be determined to what extent they are effective in causing death in individuals. The aim of this thesis is to develop a useful physician decision support model that will facilitate early treatment of patients by making it easier to predict the risk of mortality in individuals who have had a heart attack with the help of artificial intelligence-based algorithms. In this respect, the study is expected to provide clinical decision support in the management of STEMI patients and contribute to improving the quality of healthcare services.