A Deep Learning Approach to Early Detection of Heart Attacks from Health Parameters

Heart disease compromises public health and hospitals all throughout the globe. In this context, early identification of heart attacks is very crucial. Traditional diagnostic techniques are hampered by their reliance on intrusive procedures or a small range of clinical criteria. In this context, this study presents a deep learning-based model for early identification of heart attacks. Using a well-structured neural network model, our method achieves 85 % accuracy in differentiating between heart attack and normality. Although there are still difficulties like class imbalance and model fine-tuning, this study shows the potential of deep learning in enhancing early illness identification.