Long Short-Term Memory-Deep Belief Network-Based Gene Expression Data Analysis for Prostate Cancer Detection and Classification

Abstract

Prostate cancer (PRC) is the major reason of mortality globally. Early recognition and classification of PRC become essential to enhance the quality of healthcare services. A newly established deep learning (DL) and machine learning (ML) approach with different optimization tools can be employed to classify accurately of PRC accurately using microarray gene expression data (GED). Though the microarray data structures are important to diagnosing different kinds of diseases, the optimum hyperparameter tuning of the DL models poses a major challenge to achieving maximum classification performance. To resolve these issues, this study develops a new Gene Expression Data Analysis using Artificial Intelligence for Prostate Cancer Diagnoses (GEDAAI-PCD) technique. The proposed GEDAAI-PCD technique examines the GED for the identification of PRC. To accomplish this, the GEDAAI-PCD technique initially normalizes the GED into a uniform format. In addition, the long short-term memory-deep belief network (LSTM-DBN) model was applied for PRC classification purposes. The wild horse optimization (EWHO) system was utilized as a hyperparameter tuning strategy to optimize the performance of the LSTM-DBN model. The experimental assessment of the GEDAAI-PCD system occurs on open open-accessed gene expression database. The experimental outcomes emphasized the supremacy of the GEDAAI-PCD method on PRC classification.

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