چكيده به لاتين
By arrival of interdisciplinary fields, study on blood pressure signal (pulse rate) have been more taken in consideration by researchers. In this thesis, diagnosis of Coronary artery disease (CAD) has been conducted by exact measuring the signal of pulse rate non-aggressively and using statistical research and signal analysis methods which can lead to a substantial reduction in the time and cost spent on identifying the disease and even increase the accuracy of diagnosis. In this regard, in order to diagnose this disease using a type of artificial neural network-Support Vector Machine (SVM), the data of 34 men including 17 healthy men and 17 suffering from CAD has been classified. In the first step, using training data as input data, validation of the performance of SVM has been done. In this validation, 88.2% of diagnoses were carried out correctly. Next, the performance of SVM checked using test data as input data. In this step, results show CAD diagnosed with an accuracy of 82.3%. It is worth mentioning that this validation is considered as the actual validation of SVM.
Key Words: Blood Pressure Signal (Pulse Rate), Coronary Artery Disease (CAD), Disease Diagnosis, Support Vector Machine (SVM), Artificial Neural Network.
