Why the study?
Existing algorithms identify most cases of MI but tend to fail to catch early signs of the disease.
Do advanced machine learning models (GAN, DBN, GNNs) improve the early detection of myocardial infarction compared to existing algorithms?
Do advanced machine learning models (GAN, DBN, GNNs) improve the early detection of myocardial infarction compared to existing algorithms?
Advanced machine learning technologies such as GAN, DBN, and GNNs are proposed to enhance the early detection and accurate diagnosis of myocardial infarction.
May improve early MI detection accuracy in practice; extends ML comparisons beyond traditional DNN/CNN/RNN/LSTM models.
Myocardial Infarction (MI), also referred to as heart attack is leading cause of death globally, commonly caused by issues in the coronary arteries and Left Ventricle (LV) changes. Early medical care is essential to minimize the chances of death from MI. Existing algorithms are effective at identifying most cases of MI but tend to fail to catch early signs of the disease. In most cases, detection of MI depends on algorithms such as DNN [4], CNN, RNN, and LSTM. We're investigating advanced technologies like GAN, DBN, and GNNs. Such novel technologies have the potential to enhance early detection and more accurate diagnosis resulting in improved patient outcomes and reduced mortality. With these novel tools, we seek to improve the accuracy and efficacy of MI detection and intervention.
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Shanthi et al. (2025) studied this question.
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