The effectiveness of machine learning in classifying prostate cancer from high–dimensional microarray data is often constrained by feature selection instability and scalability challenges. To address these issues, we propose the Ensemble-based Distributed Filtering and Classification Model (EnD-FCM), which integrates stability-driven ensemble learning with distributed feature partitioning.