Selecting the Best Model Predicting based Data Mining Classification Algorithms for Leukemia Disease Infection

Abstract

Yearly, thousands of people die of leukemia throughout the world due to the nature of Leukemia cells that become out of control and they spread randomly and the most effective way to reduce deaths from this disease is the early discovering, and this requires an accurate diagnosis. DNA microarrays help to discover the diseases, provide accurate medical diagnosis, and help to find the right treatment and cure for many diseases. This work presents a comprehensive comparative analysis of seventeen different classification algorithms with their performance evaluation by using five performance criteria for DNA microarray dataset applied on different machines. This study focused on finding the optimum algorithm for classification of data that can predict the occurrence of leukemia disease infection in earlier stage. The results indicated that the best algorithm based on the leukemia dataset is random tree classifier with an accuracy of 100% and the total time taken to build the model is at 0.01-0.03 seconds.