Classification of Brain Volumetric Data to Determine Alzheimer's Disease Using Artificial Bee Colony Algorithm as Feature Selector

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dc.authoridKilic, Umit/0000-0001-8067-6024
dc.contributor.authorKeles, Mumine Kaya
dc.contributor.authorKılıç, Umit
dc.date.accessioned2025-01-06T17:43:32Z
dc.date.available2025-01-06T17:43:32Z
dc.date.issued2022
dc.description.abstractAlzheimer's disease is a degenerative disease that affects the age progression and causes the brain to be unable to fulfill its expected functions. Depending on the stage, the effects of Alzheimer's disease (AD) vary from forgetting the names of the surrounding people to not being able to continue daily life without assistance. To the best of our knowledge, there are currently no generally accepted diagnostic or treatment methods. In this study, a binary version of the artificial bee colony algorithm (BABC) is proposed as a feature selector for classifying AD from volumetric and statistical data of brain magnetic resonance images (MRIs). MRIs were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Volumetric and statistical data from the collected MRIs were obtained from an online system called volBrain. Then, for comparison, binary particle swarm optimization (BPSO), binary grey wolf optimization (BGWO), and binary differential evolution (BDE) were employed. For a comprehensive comparison, three algorithms, K-nearest Neighborhood (KNN), Random Forest (RF), and Support Vector Machine (SVM), are used as classifiers in feature selection progress. The results of this comparison demonstrate that BGWO outperforms BABC, which is a competitive method for this purpose. The outputs of the experiments show that all methods achieve their personal best by using RF as the classifier. Additionally, traditional data mining methods such as the Info Gain (IG), Gain Ratio (GR), Chi-square (CHI), and ReliefF methods were utilized for comparison. The results also demonstrate the superiority of the BABC over traditional methods. Another research point that this study focused on was to explore which parts of the brain are more relevant for AD diagnosis. The novelty of this study lies in the output of this point. Alongside the hippocampus and amygdala, the globus pallidus can also help in AD diagnosis.
dc.description.sponsorshipScientific Research Projects Commission Unit of Adana Alparslan Turkes Science and Technology University [18332001]; Alzheimer's Disease Neuroimaging Initiative-ADNI (National Institutes of Health) [U01 AG024904]; Department of Defense (DOD) ADNI [W81XWH-12-2-0012]
dc.description.sponsorshipThis work was supported in part by the Scientific Research Projects Commission Unit of Adana Alparslan Turkes Science and Technology University under Grant 18332001, in part by the Alzheimer's Disease Neuroimaging Initiative-ADNI (National Institutes of Health) under Grant U01 AG024904, and in part of the Department of Defense (DOD) ADNI under Award W81XWH-12-2-0012.
dc.identifier.doi10.1109/ACCESS.2022.3196649
dc.identifier.endpage83001
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85135764957
dc.identifier.scopusqualityQ1
dc.identifier.startpage82989
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2022.3196649
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2694
dc.identifier.volume10
dc.identifier.wosWOS:000842984800001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIEEE-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectMagnetic resonance imaging
dc.subjectSupport vector machines
dc.subjectDiseases
dc.subjectFeature extraction
dc.subjectData mining
dc.subjectAlzheimer's disease
dc.subjectPrediction algorithms
dc.subjectAlzheimer's disease
dc.subjectartificial bee colony
dc.subjectdata mining
dc.subjectfeature selection
dc.subjectmachine learning
dc.subjectmagnetic resonance imaging
dc.subjectswarm intelligence
dc.titleClassification of Brain Volumetric Data to Determine Alzheimer's Disease Using Artificial Bee Colony Algorithm as Feature Selector
dc.typeArticle

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