Prediction of body fat percentage using a new hybrid intelligent feature selection: ANN-KGA & ANN-IKGA
PEERJ COMPUTER SCIENCE, cilt.12, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 12
- Basım Tarihi: 2026
- Doi Numarası: 10.7717/peerj-cs.3984
- Dergi Adı: PEERJ COMPUTER SCIENCE
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Compendex, Directory of Open Access Journals, Technology Collection (ProQuest)
- Dokuz Eylül Üniversitesi Adresli: Evet
Özet
Before 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 & times; 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 R & sup2; 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 R & sup2; 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 predictive accuracy and generalization performance under strict nested validation. The results highlight the effectiveness of evolutionary feature optimization in improving BFP estimation using anthropometric measurements, providing a reliable and cost-effective alternative to specialized body analysis equipment.