Deep Learning for Early Detection of Cardiac Diseases: RNN Analysis of PTB XL+ Data

Predicting cardiac conditions is vital for early intervention and personalized care. This study explores RNN models, including LSTM and GRU, for cardiac anomaly prediction using the PTB XL+ dataset, containing 21,837 12-lead ECG recordings. The dataset, covering myocardial infarction, arrhythmias, and conduction abnormalities, enables effective training and testing of predictive models. Dataset preprocessing, standardization, and augmentation improve the generalization and robustness of the model. The research shows the revolutionary ability of deep learning in medical diagnostics, particularly with cardiac conditions using ECG data to detect and predict. RNNs will thus be able to provide early diagnosis and targeted interventions that lead to better patient outcomes. Future work would then be concentrated on optimizing model architecture, further development of advanced variants of RNN, and fine-tuning hyperparameters to improve accuracy and robustness. This research work brings out the role deep learning can play in clinical practice for enhancement and promotion of preventive care. It explains the technique behind the model's decision-making process and factors predicted for performance. This work shows how the RNN model can be used to predict heart illness using ECG data which can, in turn, be implemented in clinical practice for early diagnosis and intervention. The accuracy by the RNN model achieved here is 74.6%.