dc.contributor |
Háskólinn í Reykjavík |
dc.contributor |
Reykjavik University |
dc.contributor.author |
Ricciardi, Carlo |
dc.contributor.author |
Valente, Antonio Saverio |
dc.contributor.author |
Edmunds, Kyle |
dc.contributor.author |
Cantoni, Valeria |
dc.contributor.author |
Green, Roberta |
dc.contributor.author |
Fiorillo, Antonella |
dc.contributor.author |
Picone, Ilaria |
dc.contributor.author |
Santini, Stefania |
dc.contributor.author |
Cesarelli, Mario |
dc.date.accessioned |
2020-12-01T16:09:05Z |
dc.date.available |
2020-12-01T16:09:05Z |
dc.date.issued |
2020-01 |
dc.identifier.citation |
Ricciardi, C., Valente, A. S., Edmund, K., Cantoni, V., Green, R., Fiorillo, A., Picone, I., Santini, S., & Cesarelli, M. (2020). Linear discriminant analysis and principal component analysis to predict coronary artery disease. Health Informatics Journal, 26(3), 2181–2192. https://doi.org/10.1177/1460458219899210 |
dc.identifier.issn |
1460-4582 |
dc.identifier.issn |
1741-2811 (eISSN) |
dc.identifier.uri |
https://hdl.handle.net/20.500.11815/2266 |
dc.description |
Publisher's version (útgefin grein) |
dc.description.abstract |
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. |
dc.description.sponsorship |
The authors wish to thank Alec Shawn for his contribute as regards "grammar and spell check". This work has been realized thanks to the collaboration of the Department of Advanced Biomedical Sciences of the University Hospital "Federico II" of Naples. The authors wish to thank Sabrina De Vita, Francesca D'Agostino, Giuseppina Toscano and Tania Di Monda for their valuable contribution to the implementation of data mining algorithm during their MS thesis. The work has been partially carried out under TablHealth [CUP B49J17000720008] project and AK12 s.r.l." |
dc.format.extent |
2181-2192 |
dc.language.iso |
en |
dc.publisher |
SAGE Publications |
dc.relation.ispartofseries |
Health Informatics Journal;26(3) |
dc.rights |
info:eu-repo/semantics/openAccess |
dc.subject |
Health Informatics |
dc.subject |
Cardiology |
dc.subject |
Clinical decision-making |
dc.subject |
Data mining |
dc.subject |
Linear discriminant analysis |
dc.subject |
Principal component analysis |
dc.subject |
Heilsufarsupplýsingar |
dc.subject |
Meinafræði |
dc.subject |
Hjartasjúkdómar |
dc.subject |
Lækningar |
dc.subject |
Ákvarðanataka |
dc.subject |
Gagnanám |
dc.subject |
Fjölbreytugreining |
dc.title |
Linear discriminant analysis and principal component analysis to predict coronary artery disease |
dc.type |
info:eu-repo/semantics/article |
dcterms.license |
Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage) |
dc.description.version |
"Peer Reviewed" |
dc.identifier.doi |
10.1177/1460458219899210 |
dc.contributor.department |
Verkfræðideild (HR) |
dc.contributor.department |
Department of Engineering (RU) |
dc.contributor.school |
Tæknisvið (HR) |
dc.contributor.school |
School of Technology (RU) |