Early Heart Attack Detection Using Hybrid Deep Learning Techniques

Given the significant risk that heart disease, particularly heart attacks, poses to individuals’ lives, it is crucial to develop effective techniques for early detection. Advanced machine learning and deep learning algorithms have the ability to predict heart attacks by analyzing a patient’s medical history and overall health. These algorithms can process large datasets, extracting valuable insights that help mitigate the risk of fatal outcomes. This study integrates a deep learning approach to predict and detect heart attacks early by classifying patient data as normal or abnormal. The proposed model combines a Convolutional Neural Network (CNN) with self-attention, leveraging the self-attention mechanism to focus on the most critical aspects of the sequence. Since heart attack risk is closely tied to the changes in vital signs over time, this approach enables the model to learn and assign appropriate weights to each input component. Improvements and modifications to the hybrid model resulted in a 98.71% accuracy rate during testing. The model’s strong performance on evaluation metrics shows its potential effectiveness in detecting heart attacks.