By gaining new insights into the gene expression of individual patient profiles, clinicians and researchers can identify patterns, biomarkers and therapies. In addition, accurate classification enables the development of predictive models for prognosis and treatment response, facilitating personalized medicine approaches. Determining the optimal model for classification remains a time-consuming, nondeterministic, polynomial-time hard problem. However, the available large amount of gene expression data is too much for the traditional data analysis approaches. Therefore, a two-phase ensemble deep learning approach can be considered as a reliable framework for the root-level investigation of genomic data. In this experimental model, a gene extraction approach, a Kernel-Applied Fisher Score (KFScore) method is presented to select the prominent genomes, and a Sine-Cosine Ensemble Monarch Butterfly algorithm (SC-MBO) optimized CNN (Convolutional Neural Network) strategy is implemented for genomic data classification. Here, the SC-MBO ensemble approach is used to obtain the optimal value of hyperparameters in CNN. The effectiveness of the presented model is estimated by accuracy% of classification, number of extracted prominent genomic features, sensitivity, specificity and ROC (Receiver Operating Characteristic) curve. The effectiveness of the proposed methods is successfully tested on GSE13159, GSE15061, GSE13204, breast cancer and ovarian cancer gene expression dataset with 91.6%, 90.22%, 91.9%, 97.93% and 99.6% accuracy. The proposed model is also compared with other existing models. According to the experimental evaluation, the proposed strategy is accurate, reliable and robust. Consequently, the presented method can be treated as a trustworthy basis for disease risk prediction.

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