Cardiovascular disease prognosis using effective classification and feature selection technique

摘要: Cardiovascular disease is a worldwide health problem and according to American Heart Association (AHA), it also causes an approximate death of 17.3 million each year. Therefore early detection treatment asymptomatic cardiovascular which can significantly reduce the chances death. An important fact regarding such life-threatening prognosis identify patient's physical state (healthy or sick) based on analysis checkup data. This paper aims at optimized using different data mining techniques. We provide technique improve accuracy proposed classifier models feature selection technique. Patient's were collected from Department Computing Goldsmiths University London. dataset contains total 14 attributes in we applied SMO (SVM - Support Vector Machine), C4.5 (J48 Decision Tree) Naive Bayes classification algorithms calculated their prediction accuracy. efficient algorithm helped us model by reducing some lower ranked attributes. Which gain 87.8%, 86.80% & 79.9% case SMO, Tree respectively.