Paper Title
Applying Machine Learning Techniques for The Prediction of Heart Future Complications

Heart performance problems are always detected after analyzing the heart electrocardiogram (ECG) signal. In case of abnormal measures of the heart performance are diagnosed from the analysis, a prediction of any future complications is always needed to help doctors to follow up the case. This paper describes the application of machine learning techniques for the prediction of heart future complications. Four techniques are explained and their results are compared. These are: the Linear Prediction Method (LPM), the Grid Partitioning, Fuzzy c-mean based on Neuro-Fuzzy prediction and also GMDH-PNN. Index terms - Electrocardiograph (ECG), Heart Diseases Diagnosis, Linear Prediction Method, Grid Partitioning Method, Fuzzy c-mean (FCM), Neuro-Fuzzy (ANFIS) , Polynomial Neural Network (PNN), GMDH.