Classification problem in data mining is widely used to discover the potential information hidden in the data. Clinical, microarray data or image data related to medical field consists of high dimensions which pose difficulties for biomedical researchers in acquiring and analyzing data. Three principal challenges related to high dimensional data are Volume, Velocity and Variety. Various dimensionality reduction techniques are been used to remove irrelevant features to make the task easier and efficient. Also, using dimensionality techniques result in improved classification performance of the classifiers. This paper presents a review on the supervised machine learning algorithms for classification and prediction of various diseases. It also discusses various splitting criterion to determine the best attributes. Decision Tree algorithms are easy to understand and easy to use among all the classifiers.

Classification, CART, C4.5, C5.0, Decision tree , Dimensionality Reduction, ID3

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