Heart Disease Prediction System Evaluation Using C4.5 Rules and Partial Tree

Cardiovascular disease (CVD) is a big reason of morbidity and mortality in the current living style. Identification of Cardiovascular disease is an important but a complex task that needs to be performed very minutely and accurately and the correct automation would be very desirable. Every human being cannot be equally skilful and so as doctors. All doctors cannot be equally skilled in every sub specialty and at many places we don't have skilled and specialist doctors available easily. An automated system in medical diagnosis would enhance medical care and it can also reduce costs. In this study, we have designed a system that can efficiently discover the rules to predict the risk level of patients based on the given parameter about their health. Then we evaluate and compare this system using C45 rules and partial tree. The performance of the system is evaluated in terms of different parameter like rules generated, classification accuracy, classification error, global classification error and the experimental results shows that the system has great potential in predicting the heart disease risk level more efficiently.