4x-expert systems for early prediction of osteoporosis using multi-model algorithms

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dc.authoridHung, Bui Thanh/0000-0002-9400-7582
dc.authoridCengiz, Korhan/0000-0001-6594-8861
dc.authoridKose, Utku/0000-0002-9652-6415
dc.authoridCengiz, Korhan/0000-0001-6594-8861
dc.authoridKOTTURSAMY, KOTTILINGAM/0000-0001-8058-6416
dc.authorwosidU M, Prakash/ABE-8659-2021
dc.authorwosidHung, Bui Thanh/AAG-1384-2021
dc.authorwosidCengiz, Korhan/HTN-8060-2023
dc.authorwosidKose, Utku/C-8683-2009
dc.authorwosidCengiz, Korhan/ABD-5559-2020
dc.contributor.authorPrakash, U.
dc.contributor.authorKottursamy, Kottilingam
dc.contributor.authorCengiz, Korhan
dc.contributor.authorKose, Utku
dc.contributor.authorBui Thanh Hung
dc.date.accessioned2024-06-12T10:59:11Z
dc.date.available2024-06-12T10:59:11Z
dc.date.issued2021
dc.departmentTrakya Üniversitesien_US
dc.description.abstractOsteoporosis occurs due to micro-architectural deterioration of the bone tissues with an increased risk of bone fragility, which can cause fractures in the bone without much pressure applied to it. The T-score of a person's bone density report can be used to calculate the difference between BMD to that of healthy bones. Currently, osteoporosis is detected using conventional methods like DXA scans or high computational power requiring FEA tests. Considering individual approaches and mono-prediction techniques leads to omission of micro-fractional prediction parameters. In this paper, we have proposed a 4x-expert system for suspected osteoporosis patients, which is designed using multi model machine learning algorithms for improving prediction and accuracy through the various computational process. The experiment results shows, that the 4x-expert system covers the extensive prediction and accuracy of any suspected bone disorder patients, ranging from 75% to 97%.en_US
dc.identifier.doi10.1016/j.measurement.2021.109543
dc.identifier.issn0263-2241
dc.identifier.issn1873-412X
dc.identifier.scopus2-s2.0-85106260671en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.measurement.2021.109543
dc.identifier.urihttps://hdl.handle.net/20.500.14551/20355
dc.identifier.volume180en_US
dc.identifier.wosWOS:000663698800008en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofMeasurementen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep Learningen_US
dc.subjectMulti-Modelen_US
dc.subjectDecision Treeen_US
dc.subjectRandom Foresten_US
dc.subjectLogistic Regressionen_US
dc.subjectBone-Mineral Densityen_US
dc.subjectDiagnosisen_US
dc.subjectIdentificationen_US
dc.title4x-expert systems for early prediction of osteoporosis using multi-model algorithmsen_US
dc.typeArticleen_US

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