Efficient Colon Cancer Identification Based on Genetics Sequence Linear Support Vector Feature Selection Using Adaptive Ensemble Boosting Fuzzified Deep Neural Network

The human behaviors are varying depends on the DNA module presents in generation of human life styles. Increasing disease factors in due to DNA from history of family members continuously affects the present’s life. In the sense, the Colon Cancer (CC) is one of the deadliest diseases having the damaged cells to affect the human gene cells to damage cause deaths. The most case limitations uncovered from existing level is non mutual feature selection due to non-impact feature margin decrease the precision and identification accuracy. To resolve this problem, we propose a genetics sequence linear support vector (GSLSV) based feature selection with adaptive ensemble boosting fuzzified deep neural network (AEB-FDNN) to identify the colon cancer. Initially the preprocessing is carried by Box-plot normalization process (Bp-Np) to make noise less transformation. Then synthetic minority oversampling technique (SMOsT) is applied to identify the mutual relational features weight which is support for identifying cancer impact margins. Then colon cancer periodic influence rate (CCPIR) is applied to find the disease affection rate. With the support of marginal weights, the GSLSV based feature selection is applied to select the relation mutual features. Then the selected features is applied using AEB-FDNN to identify the CC based on the risk by class. The proposed system effectively train the dataset by improving the testing and validating accuracy as well higher performance in precision, recall rate, f1-measure and identification accuracy compared to the other system.