Pella Zuzana, Pella Dominik, Paralič Ján, Vanko Jakub Ivan, Fedačko Ján
Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Technical University of Košice, 040 01 Košice, Slovakia.
1st Department of Cardiology, East Slovak Institute for Cardiovascular Diseases, 040 01 Košice, Slovakia.
Diagnostics (Basel). 2021 Jul 16;11(7):1284. doi: 10.3390/diagnostics11071284.
Today, there are many parameters used for cardiovascular risk quantification and to identify many of the high-risk subjects; however, many of them do not reflect reality. Modern personalized medicine is the key to fast and effective diagnostics and treatment of cardiovascular diseases. One step towards this goal is a better understanding of connections between numerous risk factors. We used Factor analysis to identify a suitable number of factors on observed data about patients hospitalized in the East Slovak Institute of Cardiovascular Diseases in Košice. The data describes 808 participants cross-identifying symptomatic and coronarography resulting characteristics. We created several clusters of factors. The most significant cluster of factors identified six factors: basic characteristics of the patient; renal parameters and fibrinogen; family predisposition to CVD; personal history of CVD; lifestyle of the patient; and echo and ECG examination results. The factor analysis results confirmed the known findings and recommendations related to CVD. The derivation of new facts concerning the risk factors of CVD will be of interest to further research, focusing, among other things, on explanatory methods.
如今，有许多参数用于心血管风险量化以及识别众多高危人群；然而，其中许多参数并不能反映实际情况。现代个性化医疗是快速有效诊断和治疗心血管疾病的关键。朝着这一目标迈出的一步是更好地理解众多风险因素之间的联系。我们使用因子分析从在科希策的东斯洛伐克心血管疾病研究所住院患者的观测数据中确定合适数量的因子。该数据描述了808名参与者交叉识别的症状和冠状动脉造影结果特征。我们创建了几个因子簇。最显著的因子簇确定了六个因子：患者的基本特征；肾脏参数和纤维蛋白原；心血管疾病的家族易感性；心血管疾病个人史；患者的生活方式；以及超声心动图和心电图检查结果。因子分析结果证实了与心血管疾病相关的已知发现和建议。关于心血管疾病风险因素新事实的推导将引起进一步研究的兴趣，并将重点关注解释方法等方面。
