Analysis of A Priori Algorithm in Medical Data for Heart Disease Identification with Association Rule Mining

Heart disease is one of the leading causes of death worldwide, so it is important to identify risk factors that can contribute to the development of this disease in order to carry out early prevention. This study aims to identify patterns of association between risk variables and the incidence of heart disease using the Association Rule Mining (ARM) method combined with the A priori algorithm. The data used in this study includes lifestyle information, medical history, and other health parameters, obtained from the UCI Machine Learning repository. The analysis results showed that with a support value between 30% and 70%, the strongest association rule was found between sex (sex = 1) and angina (exang = 1), with a lift value of 1.67, indicating a strong positive relationship towards a positive diagnosis (target = 1). In addition, other moderate association rules were found, such as the combination of cp_1 = 1 and ca_0 = 1, with a lift value of about 0.73, indicating a weaker association. These findings suggest that some attribute combinations have higher predictive power, which can be used to improve prediction models in the medical diagnosis of heart disease. This research also highlights the main challenges faced by the A priori algorithm, such as computational complexity and selecting the right threshold to obtain significant rules