抽象的な

MACHINE LEARNING EVALUATION IN PAF PREDICTION

Ashraf Anwar, Said Ghoniemy

In this paper we present Support Vector Machine (SVM) and Artificial Neural Network (ANN) as two machine learning tools in classification problem concerning Normal/ PAF object in Paroxysmal Atrial Fibrillation (PAF) Prediction. PAF is a really life threatening disease and it is the result of irregular and repeated depolarization of the atria.. Using PAF prediction challenge database (afpdb), we divide the 30-min preceding the PAF into 6 periods with 5-min each. In each suggested period we get the classification result using support vector machine (SVM) and Artificial Neural Network (ANN). The performance evaluation of the two classifiers is compared in accordance of the measured sensitivity, specificity, positive predictivity and accuracy. The results indicate that the SVM classifier yields slightly higher prediction accuracy than ANN. The two classifiers realize significant results comparable to obtained results in the same field in the literature