Comparative Analysis of Frequent Pattern Mining Algorithms on Healthcare Data

The expansion of healthcare data in recent years has led to a rise in the need for effective and efficient techniques to draw insightful conclusions from vast repositories of information. Frequent pattern mining, a critical technique in data mining, plays a significant role in identifying recurring patterns, associations, and relationships within large datasets. Such patterns are particularly useful in healthcare, where they can inform clinical decision-making, enhance patient care, and optimize resource management. This study evaluates and compares four frequent pattern mining algorithms including FP-Growth, Eclat, AprioriTid, and the Partition algorithm on the MIMIC III dataset. The primary objective is to assess the performance of each algorithm in terms of efficiency and effectiveness in mining frequent itemsets and association rules from healthcare data. Our results demonstrate the strengths and limitations of each approach, providing insights into their suitability for different types of healthcare data. This comparative analysis aims to guide the selection of appropriate algorithms for mining frequent patterns in healthcare datasets.