Karaolis Minas A, Moutiris Joseph A, Hadjipanayi Demetra, Pattichis Constantinos S
Department of Computer Science, University of Cyprus, Nicosia 1678, Cyprus.
IEEE Trans Inf Technol Biomed. 2010 May;14(3):559-66. doi: 10.1109/TITB.2009.2038906. Epub 2010 Jan 12.
Coronary heart disease (CHD) is one of the major causes of disability in adults as well as one of the main causes of death in the developed countries. Although significant progress has been made in the diagnosis and treatment of CHD, further investigation is still needed. The objective of this study was to develop a data-mining system for the assessment of heart event-related risk factors targeting in the reduction of CHD events. The risk factors investigated were: 1) before the event: a) nonmodifiable-age, sex, and family history for premature CHD, b) modifiable-smoking before the event, history of hypertension, and history of diabetes; and 2) after the event: modifiable-smoking after the event, systolic blood pressure, diastolic blood pressure, total cholesterol, high-density lipoprotein, low-density lipoprotein, triglycerides, and glucose. The events investigated were: myocardial infarction (MI), percutaneous coronary intervention (PCI), and coronary artery bypass graft surgery (CABG). A total of 528 cases were collected from the Paphos district in Cyprus, most of them with more than one event. Data-mining analysis was carried out using the C4.5 decision tree algorithm for the aforementioned three events using five different splitting criteria. The most important risk factors, as extracted from the classification rules analysis were: 1) for MI, age, smoking, and history of hypertension; 2) for PCI, family history, history of hypertension, and history of diabetes; and 3) for CABG, age, history of hypertension, and smoking. Most of these risk factors were also extracted by other investigators. The highest percentages of correct classifications achieved were 66%, 75%, and 75% for the MI, PCI, and CABG models, respectively. It is anticipated that data mining could help in the identification of high and low risk subgroups of subjects, a decisive factor for the selection of therapy, i.e., medical or surgical. However, further investigation with larger datasets is still needed.
冠心病（CHD）是成年人残疾的主要原因之一，也是发达国家主要死因之一。尽管在冠心病的诊断和治疗方面已取得显著进展，但仍需进一步研究。本研究的目的是开发一种数据挖掘系统，用于评估与心脏事件相关的风险因素，目标是减少冠心病事件。所研究的风险因素包括：1）事件发生前：a）不可改变的因素——年龄、性别和早发冠心病家族史；b）可改变的因素——事件发生前吸烟、高血压病史和糖尿病病史；2）事件发生后：可改变的因素——事件发生后吸烟、收缩压、舒张压、总胆固醇、高密度脂蛋白、低密度脂蛋白、甘油三酯和血糖。所研究的事件包括：心肌梗死（MI）、经皮冠状动脉介入治疗（PCI）和冠状动脉旁路移植术（CABG）。总共从塞浦路斯帕福斯地区收集了528例病例，其中大多数有不止一次事件。使用C4.5决策树算法，针对上述三种事件，采用五种不同的划分标准进行了数据挖掘分析。从分类规则分析中提取的最重要风险因素为：1）对于MI，年龄、吸烟和高血压病史；2）对于PCI，家族史、高血压病史和糖尿病病史；3）对于CABG，年龄、高血压病史和吸烟。大多数这些风险因素也被其他研究人员提取出来。MI、PCI和CABG模型的最高正确分类百分比分别为66%、75%和75%。预计数据挖掘有助于识别高风险和低风险的受试者亚组，这是选择治疗方法（即药物治疗或手术治疗）的决定性因素。然而，仍需要使用更大的数据集进行进一步研究。
