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Feature Selection Optimization for Bioinformatics in Cancer Diagnosis Using Deep Learning Techniques

Khadija ShazlyLima HongouHakan Khan
Volume 4

Abstract

Accurate cancer diagnosis in bioinformatics relies on identifying critical biomarkers from highdimensional biological data. This study proposes a deep learning-based framework for optimizing feature selection to enhance diagnostic accuracy in cancer prediction. Utilizing autoencoders and convolutional neural networks (CNNs), the model performs dimensionality reduction and extracts salient features from genomic and transcriptomic datasets. Metaheuristic algorithms such as genetic algorithms and particle swarm optimization are integrated to refine feature subsets and prevent overfitting. The proposed approach demonstrates superior performance in classification accuracy and computational efficiency across several benchmark bioinformatics datasets, highlighting its potential for early and precise cancer diagnosis.

Keywords

Cancer diagnosis, feature selection, deep learning, bioinformatics, neural networks, optimization

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