Predicting Links in Weighted Disease Networks

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Özet

This paper proposes a link prediction method to estimate what sort of disease risks may have been carried by the patients via surveying their and similar patients' medical histories. It is possible to make future oriented predictions with link prediction by deriving new data within a network structure. It is attempted to estimate the future structure of the network and the relations which will be created or abandoned by the individuals on the basis of the nodes within the network structure and the relations between these nodes. For this purpose, an undirected weighted disease network is first created by using the data of patients which were subjected to Hemogram test at F. rat University Hospital during 2013. In the disease network, each node represents a disease and the links represent a relationship between diseases. By the developed method, then disease risk prediction is made through a proactive approach by determining what sorts of disease risks were carried by the patients applying with certain complaints. Experimental results demonstrate the applicability of the link prediction in these networks.

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AlanDeğerDil
dc.contributor.authorGül, Serpil
dc.contributor.authorKaya, Mehmet
dc.contributor.authorKaya, Buket
dc.contributor.otherIEEE
dc.date.accessioned2026-10-28T14:59:16.923519283Z
dc.date.available2026-10-28T14:59:16.923519283Z
dc.date.issued2016
dc.description.abstractThis paper proposes a link prediction method to estimate what sort of disease risks may have been carried by the patients via surveying their and similar patients' medical histories. It is possible to make future oriented predictions with link prediction by deriving new data within a network structure. It is attempted to estimate the future structure of the network and the relations which will be created or abandoned by the individuals on the basis of the nodes within the network structure and the relations between these nodes. For this purpose, an undirected weighted disease network is first created by using the data of patients which were subjected to Hemogram test at F. rat University Hospital during 2013. In the disease network, each node represents a disease and the links represent a relationship between diseases. By the developed method, then disease risk prediction is made through a proactive approach by determining what sorts of disease risks were carried by the patients applying with certain complaints. Experimental results demonstrate the applicability of the link prediction in these networks.
dc.identifier.endpage81
dc.identifier.isbn978-1-5090-2549-7
dc.identifier.startpage77
dc.identifier.urihttps://rdlopen.rawdatalibrary.net/r/munzur/4408
dc.identifier.wosWOS:000391215800014
dc.language.isoen
dc.publisherIEEE
dc.relation.conference3rd International Conference on Computer and Information Sciences (ICCOINS)
dc.relation.ispartof2016 3RD INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCES (ICCOINS)
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectDisease networks
dc.subjectLink prediction
dc.subjectSocial network analysis
dc.subjectData mining
dc.subject.classificationComputer Science, Information Systems
dc.subject.classificationComputer Science, Theory & Methods
dc.subject.classificationComputer Science
dc.titlePredicting Links in Weighted Disease Networks
dc.typeConference Object
rdl.citation.wos11
rdl.conference.dateAUG 15-17, 2016
rdl.conference.placeKuala Lumpur, MALAYSIA
rdl.oa.statusclosed
rdl.source.indexWOS
rdl.wos.indexConference Proceedings Citation Index - Science (CPCI-S)