Ricciardi Carlo, Valente Antonio Saverio, Edmund Kyle, Cantoni Valeria, Green Roberta, Fiorillo Antonella, Picone Ilaria, Santini Stefania, Cesarelli Mario
University Hospital of Naples 'Federico II', Italy.
University of Naples 'Federico II', Italy.
Health Informatics J. 2020 Sep;26(3):2181-2192. doi: 10.1177/1460458219899210. Epub 2020 Jan 23.
Coronary artery disease is one of the most prevalent chronic pathologies in the modern world, leading to the deaths of thousands of people, both in the United States and in Europe. This article reports the use of data mining techniques to analyse a population of 10,265 people who were evaluated by the Department of Advanced Biomedical Sciences for myocardial ischaemia. Overall, 22 features are extracted, and linear discriminant analysis is implemented twice through both the Knime analytics platform and R statistical programming language to classify patients as either normal or pathological. The former of these analyses includes only classification, while the latter method includes principal component analysis before classification to create new features. The classification accuracies obtained for these methods were 84.5 and 86.0 per cent, respectively, with a specificity over 97 per cent and a sensitivity between 62 and 66 per cent. This article presents a practical implementation of traditional data mining techniques that can be used to help clinicians in decision-making; moreover, principal component analysis is used as an algorithm for feature reduction.
冠状动脉疾病是现代社会中最常见的慢性疾病之一，在美国和欧洲每年都导致成千上万人死亡。本文报告了利用数据挖掘技术对10265名接受高级生物医学科学部评估的心肌缺血患者进行分析的情况。总共提取了22个特征，并通过Knime分析平台和R统计编程语言两次实施线性判别分析，将患者分为正常或病理两类。前一种分析仅包括分类，而后一种方法在分类前包括主成分分析以创建新特征。这些方法获得的分类准确率分别为84.5%和86.0%，特异性超过97%，敏感性在62%至66%之间。本文展示了传统数据挖掘技术的实际应用，可用于帮助临床医生进行决策；此外，主成分分析被用作特征约简算法。
