Prediction of Sudden Cardiac Death using Classification and Regression Tree Model with Coalesced based ECG and Clinical Data

The human heart is the most vital organ in an individual. Various techniques are used to measure heart rate and one of them is Electrocardiogram (ECG) which is first used for examining the electrical activity. One of the main reasons for an heart attack are like due to psychological problem, unhealthy foodstuff, lack of exercise, environmental factors etc, have increased the number of heart attacks rapidly among people. The research delivers an efficient to test all three data sets i.e amalgamation of clinical and ECG parameters and ECG and clinical parameters individually and the coalesced method was found most efficient in predicting an attack. This was done using a Probabilistic Neural Network (PNN) for testing the data set and based on this tested blended data Classification and Regression Tree (CART) analysis model was trained to make a prediction of the patient’s heart status.