Application of evolutionary fuzzy cognitive maps to the long-term prediction of prostate cancer

dc.contributor.author	Froelich, Wojciech	en
dc.contributor.author	Παπαγεωργίου, Ελπινίκη Ι.	el
dc.contributor.author	Σαμαρίνας, Μιχαήλ	el
dc.contributor.author	Σκριάπας, Κωνσταντίνος	el
dc.date.accessioned	2015-05-24T12:40:08Z
dc.date.available	2015-05-24T12:40:08Z
dc.date.issued	2015-05-24
dc.identifier.uri	http://hdl.handle.net/11400/11037
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://www.sciencedirect.com/science/article/pii/S1568494612000610	en
dc.subject	Prediction
dc.subject	Prostate cancer
dc.subject	Πρόληψη
dc.subject	Καρκίνος του προστάτη
dc.title	Application of evolutionary fuzzy cognitive maps to the long-term prediction of prostate cancer	en
heal.type	journalArticle
heal.generalDescription	Theoretical issues and advanced applications on Fuzzy Cognitive Maps	en
heal.classification	Medicine
heal.classification	Biomedical engineering
heal.classification	Ιατρική
heal.classification	Βιοϊατρική τεχνολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85014237
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Βιοϊατρική τεχνολογία
heal.identifier.secondary	doi:10.1016/j.asoc.2012.02.005
heal.language	en
heal.access	campus
heal.recordProvider	Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Πληροφορικής Τ.Ε.	el
heal.publicationDate	2012
heal.bibliographicCitation	Froelich, W., Papageorgiou, E., Samarinas, M. and Skriapas, K. (December 2012). Application of evolutionary fuzzy cognitive maps to the long-term prediction of prostate cancer. Applied Soft Computing. 12(12). pp. 3810-3817. Elsevier B.V.: 2012. Available from: http://www.sciencedirect.com/science/article/pii/S1568494612000610 [Accessed 06/03/2012]	en
heal.abstract	The prediction of multivariate time series is one of the targeted applications of evolutionary fuzzy cognitive maps (FCM). The objective of the research presented in this paper was to construct the FCM model of prostate cancer using real clinical data and then to apply this model to the prediction of patient's health state. Due to the requirements of the problem state, an improved evolutionary approach for learning of FCM model was proposed. The focus point of the new method was to improve the effectiveness of long-term prediction. The evolutionary approach was verified experimentally using real clinical data acquired during a period of two years. A preliminary pilot-evaluation study with 40 men patient cases suffering with prostate cancer was accomplished. The in-sample and out-of-sample prediction errors were calculated and their decreased values showed the justification of the proposed approach for the cases of long-term prediction. The obtained results were approved by physicians emerging the functionality of the proposed methodology in medical decision making.	en
heal.publisher	Elsevier B.V.	en
heal.journalName	Applied Soft Computing	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true