Classical Association Rule Mining Algorithms for Medical Data Analysis

Electronic health records (EHRs), medical imaging, and genomics are all examples of technologies that are making healthcare data grow very quickly. This makes it very hard to find new information. Association Rule Mining (ARM), a fundamental technique in data mining, has demonstrated in uncovering hidden relationships within complex medical datasets. This chapter offers an in-depth examination of classical ARM algorithms—namely Apriori, Eclat, and FP-Growth—and their role in extracting meaningful associations from clinical data. A comparative analysis is performed using medical diabetes datasets to elucidate the case study. The proposed ARM workflow consists of preprocessing, rule generation, pruning, and performance evaluation metrics such as support, confidence, and lift metrics. The experiments show that FP-Growth works better and can handle more data than Apriori and Eclat. The chapter also offers solutions, suggestions, and ideas for future research on classical ARM in medical data analysis.