关键词:
Diagnosis
摘要:
Acute myocardial infarction 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 lately and failing to intervene immediately may cause death and some permanent damage to individuals. The ST-segment elevation MI (STEMI) is one of the most serious and fatal types of acute myocardial infarction which 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. 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 respect, this study is expected to provide clinical decision support in the management of STEMI patients and contribute to improving the quality of healthcare services. In the proposed work, death risk analysis and in-hospital mortality risk prediction are carried out using some selected machine learning (ML) algorithms, such as SVM, RF, RT, k-NN, LMT, and MLP, that are proven to be effective in medical classification tasks. The conducted test results indicate that the proposed method outperforms similar studies in the literature, achieving a superior performance of over 99 % in all metrics, i.e., accuracy, recall, precision, sensitivity, F-score, and AUC. Moreover, the same competitive results have been obtained with even much fewer predictors. © 2025