In the year 1999, when T. R Golub first presented an idea for classifying cancer at the molecular level, this boosted research in cancer diagnosis to a whole new level. The researchers began to analyze the disease at the genetic level with the help of microarray databases. Then there were many new algorithms designed by researchers to classify different types of cancer. The objective of this paper is to present a tool designed exclusively to predict and classify leukemia into its types. The leukemia dataset published by Golub is used for this purpose. The first step is to identify the most significant genes causing cancer from the training set. These selected genes then are used to build the classifier based on decision rules, and eventually to predict the type of leukamia. This classifier which is modeled based on decision rules is found to work with an accuracy of 94%. The algorithm is quite simple in terms of complexity. It is possible to use a minimum number of genes for classification purposes rather than using a large set of genes. The genes that are responsible for prognosis of cancer are mainly selected for designing the classifier.