Abstract—Cardiovascular diseases (CVD), including coronary artery disease (CAD), myocardial infarction, and stroke are a group of highly prevalent and deadly diseases. The deaths from cardiovascular diseases were announced as 17.9 million in 2016 and it is expected that this number will reach approximately to 23.6 million by 2030. In order to facilitate the diagnosis and treatment of CVD, several computational approaches and data mining methods have been proposed until now. In this study, Apriori algorithm is utilized to find associations between features and rules based on UCI’s publicly available Cleveland dataset. Additionally, we generate different weighted association rules, which can help medical doctors to stratify patients and thus, propose different treatment approaches for each patient’s sub-category. Performance results show that the Apriori algorithm creates 58 rules when support and confidence parameters are set to 0.1 and 0.9, respectively. Utilizing weighted association rule approach, 6 important rules have been created based on Clinical Important Factors (CIF) and Framingham Heart Study Risk factors (FHS RF) on CVD.

Index Terms—Association rule, cardiovascular diseases, rule scoring, weighted association rules.

Gokhan Goy, Burak Kolukisa, Burcu Bakir-Gungor, Vehbi Cagri Gungor are with Deparment of Computer Engineering, Abdullah Gül University, Sümer Campus, Kayseri 38080, Turkey (email: gokhan.goy@agu.edu.tr).
Ibrahim Ugur is with Keydata Bilgi İşlem Teknoloji Sistemleri A.Ş., Ankara, Turkey.

Cite: Gokhan Goy, Burak Kolukisa, Burcu Bakir-Gungor, Ibrahim Ugur, Vehbi Cagri Gungor, "Weighted Association Rules and Scoring Methodology for Cardiovascular Diseases," International Journal of Bioscience, Biochemistry and Bioinformatics vol. 9, no. 4, pp. 222-230, 2019.