Design & Analysis of Computational Features Prediction Model for Heart Disease Diagnosis

Heart disease prediction is designed to s upport clinicians in their diagnosis. It is essenti al to find the best fit classification algorithm that has grea ter accuracy on classification in the case of heart disease prediction. Since the data is huge attribute select ion method used for reducing the dataset. Then the reduced data is given to the classification. We also propos e a new feature selection method algorithm which is the hybrid method combining CFS and RandomTree followed by part rule. The proposed algorithm provides better accuracy compared to the traditional algorit hm and the hybrid Algorithm CFS. This research paper proposed a frequent feature selection method for Heart Disease Prediction. Good performance of this method comes from the use of the RandomTree and the PART rule. The nonadditivity of the RandomTree against different target attributes measure reflects the im portance of the feature attributes as well as their interactions. Using medical profiles such as age, s ex, blood pressure and blood sugar it can predict t he likelihood of patients getting a heart disease. Clu stering the objects which have similar meaning, the proposed approach improves the accuracy and reduces the computational time.