Scalable Data Mining Using a Distributed Apriori Algorithm with Apollo Framework

Lung cancer, a critical global health issue, necessitates advanced data analysis for better detection, diagnosis, and treatment personalization. The purpose of this study is to enhance Apriori's algorithm for distributed computing using Apollo's multi-cloud orchestration framework to address challenges associated with large-scale healthcare datasets. We have demonstrated significant improvements in performance and scalability compared to traditional approaches. Based on our evaluation of real-world lung cancer data, we demonstrated substantial improvements in computational efficiency and data mining capabilities, paving the way for improved insights and customized treatments and contributing to oncology research through efficient large-scale data analysis.