Early diagnosis and accurate judgment are paramount in cancer diagnosis and treatment. Establishing an effective cancer early warning system is crucial to improve patient outcomes. Data mining technology, particularly association rule mining, plays a vital role in cancer surveillance and early warning by processing large datasets. Our study focuses on lung cancer, one of the leading causes of death worldwide. Despite numerous approaches, challenges such as high computational costs and memory limitations persist when attempting to extract meaningful rules from databases. In this paper, we propose leveraging the Apriori algorithm within the Apollo framework, based on the Apollo multicloud orchestration framework developed by the University of Innsbruck, for distributed association rule mining. By harnessing serverless functions, we achieve distributed processing, enhancing scalability and performance. Our experiments demonstrate that Apollo outperforms Apache Spark in terms of speed (about 15 percent), and extracts more rules. The results highlight the efficacy of distributed association rule mining using serverless functions for cancer early warning systems. We conclude that this approach shows promise and warrants further exploration and extension in future research endeavors.