Classical Association Rule Mining Algorithms for Medical Data

The explosive growth of medical data in recent years ranging from electronic health records to clinical trial outcomes has created a pressing need for advanced data analysis techniques that can reveal hidden disease patterns, improve diagnostic processes, and strengthen evidence-based medical practices. Among these techniques, association rule mining (ARM) has become a key tool for uncovering meaningful relationships in complex, high-dimensional healthcare datasets. This chapter aims to connect computational approaches with clinical value by demonstrating how ARM can transform raw healthcare data into actionable insights for doctors, researchers, and healthcare administrators. As medical decision-making increasingly relies on interconnected datasets including patient histories, symptoms, treatments, test results, and outcomes, ARM algorithms provide a powerful way to identify relationships that may not be immediately obvious to human experts..