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The prediction plays the important role in detecting efficient protection andtherapy of cancer. The prediction of mutations in gene needs a diagnostic andclassification, which is based on the whole database (big dataset), to reachsufficient accuracy results. Since the tumor suppressor P53 is approximatelyabout fifty percentage of all human tumors because mutations that occur in theTP53 gene into the cells. So, this paper is applied on tumor p53, where theproblem is there are several primitive databases (excel database) containdatasets of TP53 gene with its tumor protein p53, these databases are richdatasets that cover all mutations and cause diseases (cancers). But these DataBases cannot reach to predict and diagnosis cancers, i.e. the big datasets havenot efficient Data Mining method, which can predict, diagnosis the mutation,and classify the cancer of patient. The goal of this paper to reach a DataMining technique, that employs neural network, which bases on the big datasets.Also, offers friendly predictions, flexible, and effective classified cancers,in order to overcome the previous techniques drawbacks. This proposed techniqueis done by using two approaches, first, bioinformatics techniques by usingBLAST, CLUSTALW, etc, in order to know if there are malignant mutations or not.The second, data mining by using neural network; it is selected (12) out of(53) TP53 gene database fields. To clarify, one of these 12 fields (genelocation field) did not exists in TP53 gene database; therefore, it is added tothe database of TP53 gene in training and testing back propagation algorithm,in order to classify specifically the types of cancers. Feed Forward BackPropagation supports this Data Mining method with data training rate (1) andMean Square Error (MSE) (0.1). This effective technique allows ina quick, accurate and easy way to classify the type of cancer.

Effective Data Mining Technique for Classification Cancers via Mutations in Gene using Neural Network