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Early Detection of Cardiac Diseases from Electrocardiogram Using Artificial Intelligence Techniques

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Heart disease is one of the leading causes of death. Early diagnosis improves the treatment quality and reduces the mortality rates. Using Electrocardiogram (ECG) signals in health diagnosis has attracted researchers to develop a qualified intelligent medical decision support system. In this research, a smart early detection system for three common heart diseases, namely Apnea, Atrial Fibrillation (AF), and Heart Failure (HF) is developed, by analyzing the ECG signal for the patients using artificial intelligence (AI) techniques. The designed system is implemented using three different artificial intelligence techniques, namely Artificial Neural Networks (ANN), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). Electrocardiogram (ECG) signals from PhysioNet and data collected by the researchers are analyzed, and four features are extracted; then, they are used as an input for the classification algorithms. The results show the many advantages of using the proposed artificial intelligence techniques, which can result in saving the lives of patients with heart diseases. The accuracy of classification using K-nearest Neighbor is 92.4% at k=1. Using the Artificial Neural Networks provides an accuracy of 95.7% at 33 epochs, while the highest classification accuracy is obtained using Support Vector Machine with a classification accuracy of 97.8%.
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Artificial Neural Networks; Classification; ECG; K-Nearest; Medical Diagnosis; Support Vector Machine

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