A Comparative Study on Data Balancing Methods for Alzheimer's Disease Classification

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dc.contributor.authorÖter, Esma
dc.contributor.authorDoğan, Yahya
dc.date.accessioned2024-12-24T19:16:37Z
dc.date.available2024-12-24T19:16:37Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
dc.description.abstractAlzheimer's disease is a prevalent neurological disorder affecting millions of people worldwide, often associated with the aging process, leading to the death of nerve cells in the brain and loss of connections. Recently, promising results have been demonstrated in diagnosing Alzheimer's disease using deep learning models, and various approaches for early diagnosis have been proposed. However, the imbalance in health datasets, particularly those containing rare cases, can lead to performance losses and misleading results during model training. This study focuses on these imbalance issues, evaluating the effectiveness of different balancing methods using the Alzheimer's MRI dataset. In this context, the performance of SMOTE, ADASYN, and Weight Balancing methods is compared using a custom model. Experimental results indicate that, compared to the original imbalanced dataset, Weight balancing outperforms in terms of accuracy, precision, recall, and F1 score. While SMOTE and ADASYN show improvement in various metrics, they are considered inferior to the Weight Balancing method. This study contributes to selecting data-balancing methods to enhance the accuracy of deep learning models in Alzheimer's disease classification and emphasizes the importance of addressing class imbalances in health datasets.
dc.identifier.doi10.21605/cukurovaumfd.1514553
dc.identifier.endpage501
dc.identifier.issn2757-9255
dc.identifier.issue2
dc.identifier.startpage489
dc.identifier.trdizinid1250101
dc.identifier.urihttps://doi.org/10.21605/cukurovaumfd.1514553
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1250101
dc.identifier.urihttps://hdl.handle.net/20.500.12604/4508
dc.identifier.volume39
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofÇukurova Üniversitesi Mühendislik Fakültesi dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectSMOTE
dc.subjectDeep learning
dc.subjectConvolutional neural networks
dc.subjectADASYN
dc.subjectWeight balancing
dc.titleA Comparative Study on Data Balancing Methods for Alzheimer's Disease Classification
dc.typeArticle

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