INNOVATIVE APPROACH TO CANCER DIAGNOSIS THROUGH HYBRID WRAPPER-FILTER FEATURE SELECTION

Registration ID: IJNRD_215066

Published ID: IJNRD2403172

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M.AKILA, C.AJITHA

Feature Selection, Memetic Algorithm, Filters ,wrappers, genetic algorithm, symmetrical uncertainty.

Feature selection, as a preprocessing step to machine learning, is effective in reducing dimensionality, removing irrelevant data and increasing learning accuracy. The development of microarray dataset technology has supplied a large volume of data to many fields. In particular, it has been applied to prediction and diagnosis of cancer, so that it helps us to exactly predict and diagnose cancer. To precisely classify cancer we have to select genes related to cancer. The challenging task in cancer diagnosis is how to identify salient expression genes from thousands of genes in microarray data because extracted genes from microarray dataset have many unwanted datas not related to cancer. In this project we attempt explore a novel hybrid wrapper and filter feature selection algorithm for classification problem using a memetic framework i.e., a combination of genetic algorithm (GA) and local search (LS) has been proposed.. The LS is performed using correlation based filter methods are discritize, ranking and redundancy elimination with symmetrical uncertainty (SU) measure .using this hybrid method we can able find cancer related gene, From the larger amount of gene datas .using that smaller dataset doctors can able to find the affected gene and provide better treatment. The efficiency and the effectiveness of the method are demonstrated through extensive comparisons with other methods using real-world datasets of high dimentionality

Published Paper   E-Certificate

M.AKILA & C.AJITHA (March-2024). INNOVATIVE APPROACH TO CANCER DIAGNOSIS THROUGH HYBRID WRAPPER-FILTER FEATURE SELECTION. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(3), b652-b656. https://ijnrd.org/papers/IJNRD2403172.pdf

Volume 9 Issue 3, March-2024

Pages : b652-b656

Paper Reg. ID: IJNRD_215066

Published Paper Id: IJNRD2403172

Downloads: 000122254

Research Area: Engineering

Author Type: Indian Author

Country: MADURAI, TAMILNADU, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2403172.pdf

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