Analysis of Different Classifiers for Medical Dataset using Various Measures

The process of extracting information from a dataset and transforming it into an understandable structure for further use is called as data mining. A number of important techniques such as preprocessing, classification, clustering are performed in data mining using WEKA tool. In medical diagnoses the role of data mining approaches is being increased. Particularly Classification algorithms are very helpful in classifying the data, which is important for decision making process for medical practitioners. To increase the accuracy in the short time ensemble is used. The ensemble is formed by combination of two or more classifiers. For experimentation of ensembles, different types of base classifiers such as Bagging and Adaboost in combination with classifiers and classifiers such as C4.5, J48, and AD tree are used in the medical data set. The experiment is carried out in the WEKA tool on the UCI machine repository. Experimental results for ensemble with bagging classifier shows good accuracy for FT Tree in less time. Also arrthmia dataset shows the highest average accuracy.