Optimizing Predictive Models and Streamlining Feature Selection Process In Oncology

In the ever-evolving landscape of healthcare, the intersection of big data and oncology presents a realm of possibilities for advancements in diagnosis, treatment, and patient care. This abstract explores the application of Deep Bee Swarm Optimization (DBSO) in the realm of big data analytics within oncology, a novel approach poised to revolutionize data-driven decision-making. Deep Bee Swarm Optimization, inspired by the collective intelligence of bee swarms, leverages the principles of deep learning to sift through vast datasets inherent in oncological research. The algorithm's innate ability to navigate complex, high-dimensional spaces makes it particularly well-suited for the intricacies of big data in healthcare. The primary objective of this research is to harness the power of DBSO in optimizing the analysis of diverse oncological datasets, ranging from genomic information to patient records. By employing this hybrid approach, the study aims to enhance the accuracy and efficiency of predictive models, leading to more precise diagnostics and personalized treatment strategies. Furthermore, the integration of DBSO addresses the challenge of feature selection in big data analytics within oncology. The swarm intelligence enables the identification of crucial biomarkers and relevant features, streamlining the data preprocessing phase and contributing to the development of robust predictive models.