Cardio Vascular Disease (CVD) Risk Prediction using Supervised Learning

Our main goal is to develop a cardiovascular disease (CVD) risk prediction model using supervised learning classifiers that can be used in expert decision with maximum accuracy whether heart present or not. It will prove very important medicine for the diagnosis of diseases such as attack, failure, stroke and other diseases. If predictions give good results sufficient accuracy, we not only avoid inaccurate diagnoses, but also save unnecessary resources. When patient who does have diagnosed positively, he panics unnecessarily, when neither nor has negative result, dies involuntarily miss chance cure his illness. Such misdiagnosis detrimental both patients hospitals. With more accurate predictions, overcome problems.