Elektrokardiyogram kayıtlarına dayanarak kalp hastalıklarının sınıflandırılmasına yönelik sinir ağları

The precise and timely detection of cardiac arrhythmias is crucial for healthcare practitioners since it significantly impacts patient outcomes. This study is centered on the enhancement of electrocardiogram (ECG) signal classification, with a specific emphasis on deep learning and the proposed model. The complex patterns included in electrocardiogram (ECG) data, commonly used in clinical practice, pose challenges to conventional classification methods. The efficacy of the technique is evidenced by its ability to generate notable results. The classifier has been trained and validated using a comprehensive electrocardiogram (ECG) dataset that has undergone preprocessing. The performance measures (accuracy, precision, recall) underscore the exceptional ability of the system to identify cardiac arrhythmias. The validation of the model on an independent dataset reveals its capacity to generalize, maintaining a high level of accuracy and providing valuable insights into arrhythmia. The findings are supported by a comprehensive classification report, confusion matrices, and ROC analysis. This work showcases the potential of deep learning in transforming the detection of cardiac arrhythmias, using the model as an illustrative example. The results of this study contribute to the enhancement of ECG classification techniques, hence enhancing the accuracy and reliability of diagnoses and patient treatment in the field of cardiology.