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Optimistic Diagnosis of Acute Leukemia Based On Human Blood Sample Using Feed Forward Back Propagation Neural Network

Arunkumar Sivaraman, S.Arun Rajesh, Dr.M.Lakshmi

Blood cancer disease is one of the leading causes of death among men in developed and developing countries. Its cure rate and prognosis depends mainly on the early detection and diagnosis of the disease. In order to conserve the life of the individuals who are endured by the Blood cancer disease, it should be pre-diagnosed. So there is a demand of pre-diagnosis method for Blood cancer disease which should provide superior results. In this manuscript we illustrate a process to classify the microarray gene expression data based on their blood sample types using data mining and image processing techniques. The proposed Blood cancer prediagnosis system is a combination of Feed Forward Back Propagation Neural Network grouping with Statistical Approach and Fuzzy Inference System. The ultimate objective is to solve the drawbacks in dimensionality reduction as they have a direct impact on the robustness of the generated fuzzy rules. Consequently, the goal is to generate fuzzy rules based on dimensionality reduced data. Then the risk factors and the indications from the dimensional concentrated dataset are given to the Feed Forward back Propagation Neural Network to accomplish the training process. In the testing practice, more data are given to the trained fuzzy system to finalize whether the given testing data envisage the Blood disease perfectly or not.