A multi-model prediction of a stage-specific prognosis for colorectal cancer using attention-driven deep ensemble learning on genomic profiling data.

Over the decades, shifts in human lifestyle have led to alterations in dietary habits. The consumption of diets low in fiber and high in fat and sugar results in the production of carcinogenic metabolites during digestion. The food we consume undergoes a complex series of processes involving digestion and excretion, engaging various internal organs within the human body. The DNA and MiRNA present in food are crucial for sustaining human health. Damage to human organs can lead to the development of cancer cells. Among various cancers, Colorectal Cancer (CRC) is the 3rd most common cancer that contributes to the increase in the mortality rate worldwide. One of the better ways for early CRC diagnosis is through Genomic Profiling. Frequent mutations in the genes APC, TP53, KRAS, PIK3CA, and SMAD4 are observed in the collected samples, contributing to CRC. An effort has been made in the proposed work to improve classification and prediction by using a deep ensemble learning approach based on a self-attention-based stacked bidirectional LSTM, optimized with the Parallel-Whale Optimization Algorithm (WOA) for global convergence, following a feature selection process. Furthermore, the survival analysis of CRC patients is performed using the DeepSurv technique to assess treatment effectiveness and patient care. The performance of the proposed model is evaluated using various metrics for stage-based colorectal cancer prediction, and a comparative analysis is conducted to validate against benchmarking techniques, resulting in improved classification and prediction accuracy. This study may help physicians detect CRC earlier and improve patient management.