Evalauting the performance of tree based classifiers using Ebola virus dataset

Data mining have been used in real time applications due to its artificial intelligence nature. Data mining is highly used in medical domain as it helps in making better predictions and supports in decision making. It also supports physicians in developing better diagnostic treatments. We have used Data mining to analyze Ebola virus disease which leads to many deaths in South Africa. Ebola virus is very fatal and spreads due to contact with infected person. In this research work we have worked on tree based mining algorithms and further improvement is done by using filters which removes noise from the dataset. In this we worked on J48, REP, and LMT algorithms and Experimental results indicate that LMT has achieved 96.6387% accuracy rate with filtering and further fusion of algorithms is done to achieve better results.