Comparative study on decision tree based data mining algorithm to assess risk of epidemic

Forecasting the dengue fever based on the diagnosis is an important research in order to prevent and control in advance. Such assessment of risk of an epidemic based on the collected information is proposed. An automatic framework is developed for this system based on data mining. This paper present comparison of three reputed decision tree based data mining algorithms such as C4.5, LMT and REPTree for predicting the risk of dengue fever. The presented work is simulated in “weka”, a data mining tool. The performance analysis of the algorithms is compared in terms of success rate and processing time. It is observed that C4.5 out performs other two algorithms.