Analysis of Predictive Models for Cardiovascular Heart Disease Diagnosis

Medical science industry has huge amount of data, but most of this data is not mined to find out hidden information in data. Data mining techniques can be used to discover hidden patterns. Diagnosing of heart disease is one of important issue to develop medical decision support system which will help the physicians to take effective decision. In this research paper data mining classification techniques Random Decision Tree, Decision Tree Forest, Artificial neural networks (ANNs), and Support Vector Machine (SVM) are analyzed on cardiovascular disease dataset. Performance of these techniques is compared through sensitivity, specificity, accuracy, F measure, True Positive Rate, False Positive Rate and ROC. In our study 10-fold cross validation method was used to measure the unbiased estimate of these prediction models.