Leukemia cancer classification using extrusive genes from microarray data

The wide cause of death that occurs in the world is due to cancer and efficient tool diagnose microarray. essential step determine depends on selecting significant genes from dataset. This paper proposes a framework utilizing two levels selected for classifying leukemia cancer. An openly accessible microarray dataset comprising 7,129 72 patients employed this research. first level gene selection accomplished by using ReliefF technique then Independent Component Analysis applied at second acquire genes. Later are categorized as Acute Myeloid Leukemia (AML) Lymphocytic (ALL) means Random Forest, Genetic algorithm Dragonfly classifiers. It clear results classifier affords superior accuracy 94.33% also 94.96% classes. Also, techniques along with have achieved an enhanced comparison further classifiers deployed paper.