Heart disease prediction using ensemble method for feature selection

Data mining (DM) is a scheme in which useful information extracted from the unstructured data. On basis of existing information, prediction analysis (PA) method used to forecast future possibilities. This investigate work arranged on premise foreseeing cardiac illness. To pre-process data, extract attributes, and classify are all steps forecasting coronary artery disease. projected hybrid model Random Forest ensemble technique put together with Logistic Regression. The features using RF LR algorithm assisted classifying Different metrics utilized quantify model. evaluation revealed that accuracy 95% predict