Diagnosis of Alzheimer's disease using boosting classification algorithms

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dc.authorid0000-0001-8577-3659
dc.authorid0000-0001-9610-9550
dc.authorid0000-0003-1840-9958
dc.authorscopusid57205613177
dc.authorscopusid57203169526
dc.authorscopusid8945093900
dc.authorscopusid25928587900
dc.contributor.authorÖnder, Mithat
dc.contributor.authorŞentürk, Ümit
dc.contributor.authorPolat, Kemal
dc.contributor.authorPaulraj, D.
dc.date.accessioned2024-09-25T19:42:52Z
dc.date.available2024-09-25T19:42:52Z
dc.date.issued2023
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023 -- 1 November 2023 through 2 November 2023 -- Chennai -- 196097en_US
dc.description.abstractAlzheimer's Disease (AD) is a progressive degenerative disorder of the brain that impacts memory, cognition, and, ultimately, the ability to carry out daily activities. There is presently no cure for Alzheimer's Disease. However, there are available treatments to manage symptoms and slow their advancement. This research conducted a comprehensive study to diagnose AD using four different categorization methods. These methods included XGBoost, GradientBoost, AdaBoost, and voting classification algorithms. To carry out the examination, a high-quality dataset was obtained from the collection of machine learning data of the prestigious University of California. This dataset was carefully selected to ensure accurate and reliable results. The analysis of the collected data revealed some interesting findings. XGBoost exhibited an accuracy rate of 85% in diagnosing Alzheimer's Disease. ADABoost also performed, achieving an accuracy rate of 75%. GradientBoost, similarly, obtained an accuracy rate of 85%. Additionally, the voting classification algorithms showed promise, attaining an accuracy rate of 80%. All these accuracy rates were obtained by implementing a 5-fold cross-validation methodology, which ensured robust and unbiased results. This research contributes to the field of AD diagnosis by providing insights into the effectiveness of different categorization methods. © 2023 IEEE.en_US
dc.identifier.doi10.1109/RMKMATE59243.2023.10369418
dc.identifier.endpage5
dc.identifier.isbn979-835030570-8
dc.identifier.scopus2-s2.0-85183559555en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1
dc.identifier.urihttps://doi.org/10.1109/RMKMATE59243.2023.10369418
dc.identifier.urihttps://hdl.handle.net/20.500.12491/12318
dc.indekslendigikaynakScopusen_US
dc.institutionauthorÖnder, Mithat
dc.institutionauthorŞentürk, Ümit
dc.institutionauthorPolat, Kemal
dc.institutionauthorid0000-0001-8577-3659
dc.institutionauthorid0000-0001-9610-9550
dc.institutionauthorid0000-0003-1840-9958
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzYK_20240925en_US
dc.subjectADABoosten_US
dc.subjectAlzheimer's Diseaseen_US
dc.subjectGradientBoosten_US
dc.subjectXGBoosten_US
dc.titleDiagnosis of Alzheimer's disease using boosting classification algorithmsen_US
dc.typeConference Objecten_US

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