Optimizing Genomic Cancer Classification: A Concurrency‐Aware Approach to Computational Efficiency and Data Balance

ABSTRACT Medical practitioners analyze extensive genomic datasets through genomic cancer classification procedures to distinguish particular cancer types. Genomic cancer classification helps healthcare providers establish specific treatments that lead to better patient clinical results. Most of the genomic classification frameworks currently suffer from two significant issues: a reduction in the processing capabilities of large genomic data and the prevention of precise identification of rare cancer types caused by class imbalance problems. The accurate assessment of genomic cancer requires immediate attention because it will improve scalability, clinical utilization, and measurement accuracy. This research deals with genomic cancer classification inefficiencies and class imbalance through the implementation of Hadoop MapReduce and Adaptive Genomic Generative Adversarial Network (HMR‐AG‐GAN). The proposed framework implements Hadoop MapReduce distributed data processing to select features properly while executing model training with lower computational conflict. The AG‐GAN solution uses innovative dynamics to select appropriate features while maintaining genomic data integrity to create synthetic samples of rare cancer types. The AG‐GAN architecture adjusts dynamically to genomic features that matter the most; thus, it generates realistic synthetic minority class instances while preserving data genuineness. The combined algorithm method provides rapid data analysis alongside fair cancer‐type classification throughout diverse tumor groups. Research results demonstrate that AG‐GAN acts as a 45% faster computational processing technique and delivers a 15% enhancement in minority class model precision when identifying genomic cancer classes.