Toward a Reliable Classifier For Arrhythmia Diagnosis

Illnesses related to the heart are one of the major reasons for death all over the world causing many people to lose their lives in last decades. The good news is that many of those sicknesses are preventable if they are spotted in early stages. On the other hand, the number of the doctors are much lower than the number of patients. This will makes the auto diagnosing of diseases even more and more essential for humans today. Furthermore, when it comes to the diagnosing methods and algorithms, the current state of the art is lacking a comprehensive study on the comparison between different diagnosis solutions. Not having a single valid diagnosing solution has increased the confusion among scholars and made it harder for them to take further steps. This master thesis will address the issue of reliable diagnosing algorithm. We investigate ECG signals and the relation between different diseases and the heart’s electrical activity. Also, we will discuss the necessary steps needed for auto diagnosing the heart diseases including the literatures discussing the topic. The main goal of this master thesis is to find a single reliable diagnosing algorithm and quest for the best classifier to date for heart related sicknesses. Five most suited and most well-known classifiers, such as KNN, CART, MLP, Adaboost and SVM, have been investigated. To have a fair comparison, the ex-periment condition is kept the same for all classification methods. The UCI repository arrhythmia dataset will be used and the data will not be preprocessed. The experiment results indicates that AdaBoost noticeably classifies different diseases with a considera-bly better accuracy.