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.