A Multi‑Objective Metaheuristic‑Driven Feature Selection Method for Body Fat Prediction

Abstract

Accurate estimation of body fat percentage (BFP) is essential for obesity assessment and cardiovascular risk management. However, conventional anthropometric methods lack sufficient precision, while specialized body composition analyzers entail high costs that limit their widespread clinical adoption. This study proposes a multi‑objective metaheuristic‑driven feature selection framework that integrates an artificial neural network (ANN) with the Non‑Dominated Sorting Genetic Algorithm II (NSGA‑II) to optimize both predictive R2 and feature parsimony for BFP estimation. The proposed methodology is evaluated on a benchmark dataset comprising 252 individuals with 13 anthropometric predictors. A comprehensive baseline assessment is conducted using 16 machine learning algorithms, including six linear and ten non‑linear models. The hybrid ANN‑NSGA‑II framework identifies an optimal subset of six features, achieving a root mean square error (RMSE) of 3.4962 and an R‑squared (R²) value of 0.8311. These quantitative outcomes demonstrate that embedding a multi‑objective metaheuristic selection mechanism within an ANN architecture enables substantial dimensionality reduction while maintaining robust predictive performance. The proposed approach provides a cost‑effective and computationally efficient alternative for BFP prediction using only fundamental anthropometric measurements, thereby eliminating dependence on specialized body analysis equipment.

Keywords

• Body fat percentage
• Feature selection
• Multi objective optimization
• Metaheuristics
• Artificial neural network
• NSGA II

Ethical Statement

Ethics committee approval was not required for this study because of there was no study on animals or humans.

References

1. Almufti, S. M. (2019). Historical survey on metaheuristics algorithms. International Journal of Scientific World, 7(1), 1–12. https://doi.org/10.14419/ijsw.v7i1.29471
2. Aristizabal, J. C., Estrada-Restrepo, A., & Giraldo García, A. (2018). Development and validation of anthropometric equations to estimate body composition in adult women. Colombia Médica, 49(2), 154–159. https://doi.org/10.25100/cm.v49i2.3789
3. Birk, N., Kulkarni, B., Bhogadi, S., Aggarwal, A., Walia, G. K., Gupta, V., Rani, U., Mahajan, H., Kinra, S., & Mallinson, P. A. (2025). Machine learning-based equations for improved body composition estimation in Indian adults. PLOS Digital Health, 4(6), Article e0000671. https://doi.org/10.1371/journal.pdig.0000671
4. Botchkarev, A. (2018). Performance metrics (error measures) in machine learning regression, forecasting and prognostics: Properties and typology [Preprint]. arXiv. https://doi.org/10.48550/arXiv.1809.03006
5. Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. https://doi.org/10.1007/BF00058655
6. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
7. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017). Classification and regression trees. Routledge. https://doi.org/10.1201/9781315139470
8. Burke, E. K., & Kendall, G. (Eds.). (2005). Search methodologies: Introductory tutorials in optimization and decision support techniques. Springer. https://doi.org/10.1007/0-387-28356-0

9. Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247–1250. https://doi.org/10.5194/gmd-7-1247-2014
10. Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., & Chen, K. (2015). Xgboost: Extreme gradient boosting (R package version 0.4-2). CRAN.
11. Chinneck, J. (2006). Practical optimization: A gentle introduction. Carleton University.
12. Chiong, R. (Ed.). (2009). Nature-inspired algorithms for optimisation (Vol. 193). Springer. https://doi.org/10.1007/978-3-642-00267-0
13. Chiong, R., Fan, Z., Hu, Z., & Chiong, F. (2021). Using an improved relative error support vector machine for body fat prediction. Computer Methods and Programs in Biomedicine, 198, Article 105749. https://doi.org/10.1016/j.cmpb.2020.105749
14. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018
15. Das, K., Jiang, J., & Rao, J. (2004). Mean squared error of empirical predictor. The Annals of Statistics, 32(2), 818–840. https://doi.org/10.1214/009053604000000201
16. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197. https://doi.org/10.1109/4235.996017
17. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002a). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197.
18. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002b). Nsga-ii. IEEE transactions on evolutionary computation, 6(2), 182-197.
19. Dokeroglu, T., Deniz, A., & Kiziloz, H. E. (2022). A comprehensive survey on recent metaheuristics for feature selection. Neurocomputing, 494, 269–296. https://doi.org/10.1016/j.neucom.2022.04.083
20. Drucker, H., Burges, C. J., Kaufman, L., Smola, A., & Vapnik, V. (1996). Support vector regression machines. In M. C. Mozer, M. I. Jordan, & T. Petsche (Eds.), Advances in neural information processing systems (Vol. 9, pp. 155–161). MIT Press.
21. Ellis, K. J. (2000). Human body composition: In vivo methods. Physiological Reviews, 80(2), 649–680. https://doi.org/10.1152/physrev.2000.80.2.649
22. Figueiredo Filho, D. B., Júnior, J. A. S., & Rocha, E. C. (2011). What is R2 all about? Leviathan (São Paulo), (3), 60–68. https://doi.org/10.11606/issn.2237-4485.lev.2011.132273
23. Fix, E., & Hodges, J. L. (1989). Discriminatory analysis. Nonparametric discrimination: Consistency properties. International Statistical Review / Revue Internationale de Statistique, 57(3), 238–247. https://doi.org/10.2307/1403797
24. Freedman, D. S., Dietz, W. H., Srinivasan, S. R., & Berenson, G. S. (2009). Risk factors and adult body mass index among overweight children: The Bogalusa Heart Study. Pediatrics, 123(3), 750–757. https://doi.org/10.1542/peds.2008-1280
25. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232. https://doi.org/10.1214/aos/1013203451
26. Gruzdeva, O., Borodkina, D., Uchasova, E., Dyleva, Y., & Barbarash, O. (2018). Localization of fat depots and cardiovascular risk. Lipids in Health and Disease, 17(1), Article 218. https://doi.org/10.1186/s12944-018-0856-8
27. Hastuti, J., Kagawa, M., Byrne, N. M., & Hills, A. P. (2018). Anthropometry to assess body fat in Indonesian adults. Asia Pacific Journal of Clinical Nutrition, 27(3), 592–598. https://doi.org/10.6133/apjcn.062017.06
28. Henry, C. J., Shalini, D., Ponnalagu, O., Bi, X., & Tan, S.-Y. (2018). New equations to predict body fat in Asian-Chinese adults using age, height, skinfold thickness, and waist circumference. Journal of the Academy of Nutrition and Dietetics, 118(7), 1263–1269. https://doi.org/10.1016/j.jand.2017.09.020
29. Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55–67. https://doi.org/10.1080/00401706.1970.10488634
30. Hua, J., Tembe, W. D., & Dougherty, E. R. (2009). Performance of feature-selection methods in the classification of high-dimension data. Pattern Recognition, 42(3), 409–424. https://doi.org/10.1016/j.patcog.2008.08.001
31. Imai, A., Komatsu, S., Ohara, T., Kamata, T., Yoshida, J., Miyaji, K., Takewa, M., & Kodama, K. (2012). Visceral abdominal fat accumulation predicts the progression of noncalcified coronary plaque. Atherosclerosis, 222(2), 524–529. https://doi.org/10.1016/j.atherosclerosis.2012.03.017
32. Imran, M., & Alsuhaibani, S. A. (2019). A neuro-fuzzy inference model for diabetic retinopathy classification. In M. A. Jabbar, S. Tiwari, K. S. Kaswan, & S. N. Kumar (Eds.), Intelligent data analysis for biomedical applications (pp. 147–172). Elsevier. https://doi.org/10.1016/B978-0-12-815553-0.00008-3
33. Johnson, R. W. (1996). Fitting percentage of body fat to simple body measurements. Journal of Statistics Education, 4(1). https://doi.org/10.1080/10691898.1996.11910505
34. Kalousis, A., Prados, J., & Hilario, M. (2007). Stability of feature selection algorithms: A study on high-dimensional spaces. Knowledge and Information Systems, 12(1), 95–116. https://doi.org/10.1007/s10115-006-0040-8
35. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Advances in neural information processing systems (Vol. 30, pp. 3146–3154). Curran Associates, Inc.
36. Kopelman, P. (2007). Health risks associated with overweight and obesity. Obesity Reviews, 8(S1), 13–17. https://doi.org/10.1111/j.1467-789X.2007.00311.x
37. Lai, C.-M., Chiu, C.-C., Shih, Y.-C., & Huang, H.-P. (2022). A hybrid feature selection algorithm using simplified swarm optimization for body fat prediction. Computer Methods and Programs in Biomedicine, 226, Article 107183. https://doi.org/10.1016/j.cmpb.2022.107183
38. Lichtner-Bajjaoui, A. (2021). A mathematical introduction to neural networks. Independently Published.
39. McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115–133. https://doi.org/10.1007/BF02478259
40. Nascimento, N. M. M., de Sousa Junior, P. C., Nunes, P. Y. R., da Silva, S. P. P., Loureiro, L. L., Bittencourt, V. Z., Junior, V. L. M. C., & Rebouças Filho, P. P. (2025). New advances in body composition assessment with ShapedNet: A single image deep regression approach. Pattern Recognition Letters, 188, 88–94. https://doi.org/10.1016/j.patrec.2024.12.012
41. Nguemdjom, D., Mbayandjambe, A., Nkwimi, G., Oshasha, F., Muluba, C., Mbengandji, H., & Bazie, I. (2025). Explainable AI (XAI) for obesity prediction: An optimized MLP approach with SHAP interpretability on lifestyle and behavioral data. International Journal of Innovative Science and Research Technology, 10(4), 3192–3200.
42. Ólafsson, S. (2006). Metaheuristics. In S. G. Henderson & B. L. Nelson (Eds.), Handbooks in operations research and management science (Vol. 13, pp. 633–654). Elsevier. https://doi.org/10.1016/S0927-0507(06)13021-2
43. Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: Unbiased boosting with categorical features. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, & R. Garnett (Eds.), Advances in neural information processing systems (Vol. 31, pp. 6638–6648). Curran Associates, Inc.
44. Rishiwal, V., Agarwal, S., Mantoro, T., Yadav, M., Yadav, P., & Sharma, S. (2024). ML-based investigation of hybrid regressor model for accurate body fat and BMI prediction. In 2024 10th International Conference on Computing, Engineering and Design (ICCED) (pp. 1–6). IEEE. https://doi.org/10.1109/ICCED63595.2024.10842247
45. Salamunes, A. C. C., Stadnik, A. M. W., & Neves, E. B. (2018). Estimation of female body fat percentage based on body circumferences. Revista Brasileira de Medicina do Esporte, 24(2), 97–101. https://doi.org/10.1590/1517-869220182402170329
46. Sengupta, R. N., Gupta, A., & Dutta, J. (Eds.). (2016). Decision sciences: Theory and practice. CRC Press. https://doi.org/10.1201/b21580
47. Shao, Y. E. (2014). Body fat percentage prediction using intelligent hybrid approaches. The Scientific World Journal, 2014, Article 649692. https://doi.org/10.1155/2014/649692
48. Srinivas, N., & Deb, K. (1994). Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, 2(3), 221–248. https://doi.org/10.1162/evco.1994.2.3.221
49. Svendsen, O. L., Haarbo, J., Heitmann, B. L., Gotfredsen, A., & Christiansen, C. (1991). Measurement of body fat in elderly subjects by dual-energy X-ray absorptiometry, bioelectrical impedance, and anthropometry. The American Journal of Clinical Nutrition, 53(5), 1117–1123. https://doi.org/10.1093/ajcn/53.5.1117
50. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
51. Tranmer, M., & Elliot, M. (2008). Multiple linear regression (Cathie Marsh Centre for Census and Survey Research Paper 2008-19). University of Manchester.
52. Uçar, M. K., Ucar, Z., Köksal, F., & Daldal, N. (2021). Estimation of body fat percentage using hybrid machine learning algorithms. Measurement, 167, Article 108173. https://doi.org/10.1016/j.measurement.2020.108173
53. Wang, L., Ni, H., Yang, R., Pappu, V., Fenn, M. B., & Pardalos, P. M. (2014). Feature selection based on meta-heuristics for biomedicine. Optimization Methods and Software, 29(4), 703–719. https://doi.org/10.1080/10556788.2013.840250
54. Wang, Y. C., McPherson, K., Marsh, T., Gortmaker, S. L., & Brown, M. (2011). Health and economic burden of the projected obesity trends in the USA and the UK. The Lancet, 378(9793), 815–825. https://doi.org/10.1016/S0140-6736(11)60814-3
55. Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79–82. https://doi.org/10.3354/cr030079
56. Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109–130. https://doi.org/10.1016/S0169-7439(01)00155-1
57. World Health Organization. (2018). Noncommunicable diseases country profiles 2018. World Health Organization.
58. Yağmur, N. (2024). A hybrid approach to obesity level determination with decision tree and pelican optimization algorithm. Journal of Scientific Reports-A, (57), 97–109.
59. Yang, X.-S. (2010). Nature-inspired metaheuristic algorithms (2nd ed.). Luniver Press.
60. Yusta, S. C. (2009). Different metaheuristic strategies to solve the feature selection problem. Pattern Recognition Letters, 30(5), 525–534. https://doi.org/10.1016/j.patrec.2008.11.012
61. Zheng, Y., Long, Z., Feng, B., Cheng, R., Vaziri, K., & Hahn, J. K. (2024). D3BT: Dynamic 3D body transformer for body fat percentage assessment. IEEE Journal of Biomedical and Health Informatics, 29(2), 848–856. https://doi.org/10.1109/JBHI.2024.3477123
62. Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301–320. https://doi.org/10.1111/j.1467-9868.2005.00503.x