Gokhan Goy, Burak Kolukisa, Bakir-Gungor Burcu, I. Ugur, V. C. Gungor
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引用次数: 0
摘要
心血管疾病(CVD),包括冠状动脉疾病(CAD)、心肌梗死和中风是一组高发和致命的疾病。2016年,心血管疾病导致的死亡人数为1790万,预计到2030年,这一数字将达到约2360万。为了方便心血管疾病的诊断和治疗,目前已经提出了几种计算方法和数据挖掘方法。在本研究中,基于UCI公开的Cleveland数据集,利用Apriori算法寻找特征和规则之间的关联。此外,我们生成了不同的加权关联规则,这可以帮助医生对患者进行分层,从而为每个患者的子类别提出不同的治疗方法。性能结果表明,当支持度和置信度参数分别设置为0.1和0.9时,Apriori算法创建了58条规则。利用加权关联规则方法,基于临床重要因素(CIF)和Framingham Heart Study Risk Factors (FHS RF)对CVD的影响,建立了6条重要规则。
Weighted Association Rules and Scoring Methodology for Cardiovascular Diseases
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.