Multi-stage biomedical feature selection extraction algorithm for cancer detection

Abstract Cancer is a significant cause of death worldwide. Early cancer detection greatly aided by machine learning and artificial intelligence (AI) to gene microarray data sets (microarray data). Despite this, there discrepancy between the number features in set samples. Because it crucial identify markers for array data. Existing feature selection algorithms, however, generally use long-standing, are limited single-condition rarely take extraction into account. This work proposes Multi-stage algorithm Biomedical Deep Feature Selection (MBDFS) address this issue. In first, three techniques combined thorough selection, subsets obtained; second, an unsupervised neural network used create best representation subset enhance final classification accuracy. Using variety metrics, including comparison results before after performance alternative methods, we evaluate MBDFS's efficacy. The experiments demonstrate that although MBDFS uses fewer features, accuracy either unchanged or enhanced.