Cardiovascular Heart Disease Diagnosis Using Improved Decision Tree C4.5 Predictive Data Mining Model

Medical science industry has huge amount of data, but unfortunately most of this data is not mined to find out hidden information in data. Advanced data mining techniques can be used to discover hidden pattern in data. Models developed from these techniques will be useful for medical practitioners to take effective decision. In this research paper data mining classification techniques Decision Tree C4.5 and Improved Decision Tree C4.5 are analyzed on cardiovascular disease dataset. Performance of these techniques is compared through sensitivity, specificity, accuracy, True Positive Rate and False Positive Rate. In our studies 10-fold cross validation method was used to measure the unbiased estimate of these prediction models. Studies showed that improved C4.5 algorithm gave better results than C.45 algorithm on each dataset where it was applied.