Analyzing the suitability of relevant classification techniques on medical data set for better prediction

With the upcoming trend of online transaction in all sectors amount of newly created information increases every year. The huge amount of information has made it unfeasible for data analysts to achieve a deeper understanding of their data without at least some form of computer-aid. Data mining can be used to mechanize the process of knowledge discovery from databases. One of the techniques used in data mining is classification. Data mining classification algorithms can follow three special learning approaches: semi-supervised learning, supervised learning and unsupervised learning. In this paper we apply and analyze the commonly used classification algorithms on medical data set that helps to predict heart disease that accounts to be the primary cause of death worldwide. It is complex for medical practitioners to envisage the heart attack as it requires experience and knowledge. The health sector today contains concealed yet significant information for making decisions. Experiments conducted reveal that algorithm such as J48, SIMPLE CART and REPTREE provides more predictive accuracy than other algorithms.