Explainable Model of Hybrid Ensemble Learning for Prostate Cancer RNA-Seq Classification via Targeted Feature Selection

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High dimensional, small sample ribonucleic acid sequencing (RNA-seq) data pose a major challenge for reliable classification due to the curse of dimensionality and the risk of overfitting. This study addresses that challenge for prostate cancer by coupling machine learning (ML) based feature selection with a hybrid ensemble classifier. RNA-seq datasets retrieved from the University of California Santa Cruz (UCSC) Xena platform have been pre-processed, and the number of features has been reduced to 30 through systematic feature selection. Three complementary learners have been combined into a majority voting ensemble to improve robustness and generalization. Performance has been assessed using evaluation metric criteria and the area under the curve. The results have been corroborated on liver, lung, and thyroid cancer datasets from the literature. The proposed hybrid ensemble method has been achieved 97.82% accuracy with Lasso feature selection. Compared with conventional single model approaches, the proposed hybrid model has yielded accurate and reliable predictions. Additionally, biologically meaningful information has been presented using explainable artificial intelligence techniques regarding the importance of genes. These findings suggest that the joint use of feature selection and hybrid ensembles provides a practical and interpretable framework for classifying high dimensional genomic profiles under limited sample sizes.