Prediction of body fat percentage using a new hybrid intelligent feature selection: ANN-KGA & ANN-IKGA

Basit Öğe Kaydı
dc.authorid0000-0001-6415-0698
dc.contributor.authorVarlıklar, Özlem
dc.date.accessioned2026-07-22T10:05:11Z
dc.date.available2026-07-22T10:05:11Z
dc.date.issued07/02/2026
dc.departmentKapadokya Üniversitesi
dc.description.abstractBefore initiating obesity treatment, it is essential to determine body fat percentage (BFP), a measure that cannot be assessed through conventional weighing alone. To address this, specialized “Body Analyzers” have been developed; however, their high cost encourages the search for more practical and cost-effective alternatives. In this study, 16 machine learning algorithms (six linear and 10 non-linear) were evaluated for BFP prediction and compared with two hybrid evolutionary frameworks combining an Artificial Neural Network (ANN) with a K-means Genetic Algorithm (ANN-KGA) and an Improved K-means Genetic Algorithm (ANN-IKGA). To ensure methodological rigor and prevent optimistic bias, a strict nested cross-validation strategy (five outer folds× three inner folds) was employed during feature selection and model evaluation. To account for the stochastic nature of the genetic algorithm, the entire nested cross-validation procedure was repeated 15 times with different random seeds. The ANN-KGA model achieved a mean outer-fold R² of 0.7522 (95% CI [0.6963–0.8081]) with a mean Root Mean Squared Error (RMSE) of 3.9651 (95% CI [3.5453–4.3849]) across the 15 runs. The proposed ANN-IKGA model demonstrated strong predictive performance, achieving a mean outer-fold R² of 0.7858 (95% CI [0.7495–0.8222]) and a mean RMSE of 3.7182 (95% CI [3.1777–4.2586]) across the 15 independent runs. Statistical comparison against baseline models using paired t-tests indicated significant improvement (p< 0.05) for both evolutionary approaches. These findings demonstrate that the proposed ANN-IKGA framework enhances …
dc.identifier.citationAPA 7
dc.identifier.doihttp://dx.doi.org/10.7717/peerj-cs.3984
dc.identifier.endpage40
dc.identifier.issn2376-5992
dc.identifier.issuee3984
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/20.500.12695/4249
dc.identifier.volume12
dc.identifier.wosWOS:001817186800001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.institutionauthorYousefi, Tohid
dc.institutionauthorid0000-0003-4288-8194
dc.language.isoen
dc.publisherPeerJ Inc.
dc.relation.ispartofPeerJ Computer Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectBody fat percentage
dc.subjectBody fat prediction
dc.subjectData processing
dc.subjectFeature selection
dc.subjectImproved K-means genetic algorithm
dc.subjectK-means genetic algorithm
dc.subjectMachine learning
dc.subjectArtificial neural network
dc.subjectMeta-heuristic algorithms
dc.titlePrediction of body fat percentage using a new hybrid intelligent feature selection: ANN-KGA & ANN-IKGA
dc.typeArticle

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
peerj-cs-3984.pdf
Boyut:
6.87 MB
Biçim:
Adobe Portable Document Format
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
license.txt
Boyut:
1.17 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Yapay Zeka ve Makine Öğrenmesi