dc.contributor.author | Lambrou, Alexandra | en |
dc.contributor.author | Papadopoulos, Harris | en |
dc.contributor.author | Kyriacou, Efthyvoulos C. | en |
dc.contributor.author | Pattichis, Constantinos S. | en |
dc.contributor.author | Pattichis, Marios S. | en |
dc.contributor.author | Gammerman, A. | en |
dc.contributor.author | Nicolaïdes, Andrew N. | en |
dc.creator | Lambrou, Alexandra | en |
dc.creator | Papadopoulos, Harris | en |
dc.creator | Kyriacou, Efthyvoulos C. | en |
dc.creator | Pattichis, Constantinos S. | en |
dc.creator | Pattichis, Marios S. | en |
dc.creator | Gammerman, A. | en |
dc.creator | Nicolaïdes, Andrew N. | en |
dc.date.accessioned | 2019-11-13T10:40:54Z | |
dc.date.available | 2019-11-13T10:40:54Z | |
dc.date.issued | 2010 | |
dc.identifier.issn | 1868-4238 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/54354 | |
dc.description.abstract | Non-invasive ultrasound imaging of carotid plaques allows for the development of plaque image analysis in order to assess the risk of stroke. In our work, we provide reliable confidence measures for the assessment of stroke risk, using the Conformal Prediction framework. This framework provides a way for assigning valid confidence measures to predictions of classical machine learning algorithms. We conduct experiments on a dataset which contains morphological features derived from ultrasound images of atherosclerotic carotid plaques, and we evaluate the results of four different Conformal Predictors (CPs). The four CPs are based on Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Naive Bayes classification (NBC), and k-Nearest Neighbours (k-NN). The results given by all CPs demonstrate the reliability and usefulness of the obtained confidence measures on the problem of stroke risk assessment. © 2010 IFIP. | en |
dc.source | 6th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2010 | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-78549281407&doi=10.1007%2f978-3-642-16239-8_21&partnerID=40&md5=615063fc24cd62bb0b633439244e38dd | |
dc.subject | Risk assessment | en |
dc.subject | Learning algorithms | en |
dc.subject | Forecasting | en |
dc.subject | Neural networks | en |
dc.subject | Data processing | en |
dc.subject | Ultrasonics | en |
dc.subject | Image analysis | en |
dc.subject | Support vector machines | en |
dc.subject | Confidence Measure | en |
dc.subject | confidence measures | en |
dc.subject | carotid plaques | en |
dc.subject | Conformal Prediction | en |
dc.subject | stroke risk assessment | en |
dc.subject | ultrasound images | en |
dc.title | Assessment of stroke risk based on morphological ultrasound image analysis with conformal prediction | en |
dc.type | info:eu-repo/semantics/article | |
dc.identifier.doi | 10.1007/978-3-642-16239-8_21 | |
dc.description.volume | 339 AICT | en |
dc.description.startingpage | 146 | |
dc.description.endingpage | 153 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Article | en |
dc.description.notes | <p>Sponsors: Cyprus University of Technology | en |
dc.description.notes | Frederick University | en |
dc.description.notes | Cyprus Tourism Organization | en |
dc.description.notes | Conference code: 82446 | en |
dc.description.notes | Cited By :11</p> | en |
dc.source.abbreviation | IFIP Advances in Information and Communication Technology | en |
dc.contributor.orcid | Pattichis, Constantinos S. [0000-0003-1271-8151] | |
dc.contributor.orcid | Pattichis, Marios S. [0000-0002-1574-1827] | |
dc.contributor.orcid | Kyriacou, Efthyvoulos C. [0000-0002-4589-519X] | |
dc.gnosis.orcid | 0000-0003-1271-8151 | |
dc.gnosis.orcid | 0000-0002-1574-1827 | |
dc.gnosis.orcid | 0000-0002-4589-519X | |