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Original Article
Stacked ensemble learning with XAI for Accurate Obesity level Prediction
G.M.G. Madhuri1
Tumu Venkata Prasanna Lakshmi2
Simhadri Harsha Vardhan3
Pasala Preethi4
Tipparti Jahnavi5
Salagala Seshu6
1 Assistant Professor, Department of Computer Science and Engineering Dhanekula Institute of Engineering and Technology, Ganguru, India. 2 3 4 5 6 Department of Computer Science and Engineering Dhanekula Institute of Engineering and Technology, Ganguru, India.
Published Online: March-April 2026
Pages: 175-189
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20260602024
References
1. R. Kaur, R. Kumar, and M. Gupta, ‘‘Predicting risk of obesity and meal planning to reduce the obese in adulthood using artificial
intelligence,’’ Endocrine, vol. 78, no. 3, pp. 458–469, Oct. 2022, doi: 10.1007/s12020- 022-03215-4.
2. (2023). World Health Organization. [Online]. Available: https://www.who.int/health-topics/obesity
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Rev. Environ. Health, vol. 30, no. 3, pp. 191–205, 2015, doi: 10.1515/reveh-2015-0008.
4. Z. A. Memish, C. El Bcheraoui, M. Tuffaha, M. Robinson, F. Daoud, S. Jaber, S. Mikhitarian, M. Al Saeedi, M. A. AlMazroa, A. H.Mokdad, and A. A. Al Rabeeah, ‘‘Obesity and associated factors—Kingdom of Saudi Arabia, 2013,’’ Preventing Chronic Disease, vol.
11, p. E174, Oct. 2014, doi: 10.5888/pcd11.140236.
5. F. A. Hamam, A. S. Eldalo, A. A. Alnofeie, W. Y. Alghamdi, S. S. Almutairi, and F. S. Badyan, ‘‘The association of eating habits and
lifestyle with overweight and obesity among health sciences students in taif university, KSA,’’ J. Taibah Univ. Med. Sci., vol. 12, no. 3,
pp. 249–260, Jun. 2017. [Online]. Available: https://www. sciencedirect.com/science/article/pii/S1658361216301494
6. S. K. Keadle, R. McKinnon, B. I. Graubard, and R. P. Troiano, ‘‘Prevalence and trends in physical activity among older adults in the United
States: A comparison across three national surveys,’’ Preventive Med., vol. 89, pp. 37–43, Aug. 2016.
7. A. C. Morrill and C. D. Chinn, ‘‘The obesity epidemic in the United States,’’ J. Public Health Policy, vol. 25, nos. 3–4, pp. 353–366, Dec.
2004, doi: 10.1057/palgrave.jphp.3190035.
8. Estimation of Obesity Levels Based on Eating Habits and Physical Condition, UCI Machine Learning Repository, Irvine, CA, USA, 2019,
doi: 10.24432/C5H31Z.
9. F. M. Palechor and A. D. L. H. Manotas, ‘‘Dataset for estimation of obesity levels based on eating habits and physical condition in
individuals from colombia, Peru and Mexico,’’ Data Brief, vol. 25, Aug. 2019, Art. no. 104344.
10. G. Shao, ‘‘Comparison of prediction of obesity status based on different machine learning approaches with different factor quantities,’’ in
Proc. Int. Conf. Biomed. Intell. Syst. (IC-BIS), Dec. 2022, p. 144.
11. I. G. S. M. Diayasa, M. Idhom, A. Fauzi, and A. T. Damaliana, ‘‘Stacking ensemble methods to predict obesity levels in adults,’’ in Proc.
IEEE 8th Inf. Technol. Int. Seminar (ITIS), Oct. 2022, pp. 339–344
12. Z. Zheng and K. Ruggiero, ‘‘Using machine learning to predict obesity in high school students,’’ in Proc. IEEE Int. Conf. Bioinf. Biomed.
(BIBM), Nov. 2017, pp. 2132–2138.
13. H. B. Kibria, M. Nahiduzzaman, M. O. F. Goni, M. Ahsan, and J. Haider, ‘‘An ensemble approach for the prediction of diabetes mellitus
using a soft voting classifier with an explainable AI,’’ Sensors, vol. 22, no. 19, p. 7268, Sep. 2022.
14. M. J. Raihan, M. A. M. Khan, S. H. Kee, and A. A. Nahid, ‘‘Detection of the chronic kidney disease using XGBoost classifier and
explaining the influence of the attributes on the model using SHAP,’’ Sci. Rep., vol. 13, no. 1, p. 6263, Apr. 2023, doi: 10.1038/s41598-
023-33525-0.
15. S. Jahan, K. A. Taher, M. S. Kaiser, M. Mahmud, M. S. Rahman, A. S. M. S. Hosen, and I. Ra, ‘‘Explainable AI-based Alzheimer’s
prediction and management using multimodal data,’’ PLoS ONE, vol. 18, Nov. 2023, Art. no. 0294253.
16. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees. Monterey, CA, USA: Wadsworth and
Brooks, 1984
17. Sharma, A., and S. Gupta (2022). “An ensemble approach for predicting obesity based on lifestyle and demographic factors.” DOI:
10.1155/2022/4549320; Journal of Healthcare Engineering, 2022, 1 - 10.
18. Ameen, S., & Hossain, M. M (2022). “A machine learning approach for obesity prediction among adolescents: A case study in Bangladesh.”
DOI: 10.3390/healthcare10040705. Healthcare, 10 (4) 705.
19. Tran, D. T., & Le, T. N (2023). “Ensemble learning for predicting obesity risk: A case study of Vietnamese adolescents.” 23 (1), 1 - 11.
doi: 10.1186/s12889-023-16273-5. BMC Public Health.
20. In 2023, Alhassan, S. I., and Majid, M. A published “Obesity prediction using ensemble machine learning techniques: A case study of adult
populations.” 29 (1): 146–157 in the Health Informatics Journal; DOI: 10.1177/14604582211057523.
21. In 2023, A. Subramanian and P. Karthikeyan published “Machine learning in healthcare: A comprehensive review of the applicati ons in
obesity prediction.” Medical Artificial Intelligence, 127, 102914, 10.1016/j.artmed.2022.102914
intelligence,’’ Endocrine, vol. 78, no. 3, pp. 458–469, Oct. 2022, doi: 10.1007/s12020- 022-03215-4.
2. (2023). World Health Organization. [Online]. Available: https://www.who.int/health-topics/obesity
3. E. DeNicola, O. S. Aburizaiza, A. Siddique, H. Khwaja, and D. O. Carpenter, ‘‘Obesity and public health in the kingdom of Saudi Arabia,’’
Rev. Environ. Health, vol. 30, no. 3, pp. 191–205, 2015, doi: 10.1515/reveh-2015-0008.
4. Z. A. Memish, C. El Bcheraoui, M. Tuffaha, M. Robinson, F. Daoud, S. Jaber, S. Mikhitarian, M. Al Saeedi, M. A. AlMazroa, A. H.Mokdad, and A. A. Al Rabeeah, ‘‘Obesity and associated factors—Kingdom of Saudi Arabia, 2013,’’ Preventing Chronic Disease, vol.
11, p. E174, Oct. 2014, doi: 10.5888/pcd11.140236.
5. F. A. Hamam, A. S. Eldalo, A. A. Alnofeie, W. Y. Alghamdi, S. S. Almutairi, and F. S. Badyan, ‘‘The association of eating habits and
lifestyle with overweight and obesity among health sciences students in taif university, KSA,’’ J. Taibah Univ. Med. Sci., vol. 12, no. 3,
pp. 249–260, Jun. 2017. [Online]. Available: https://www. sciencedirect.com/science/article/pii/S1658361216301494
6. S. K. Keadle, R. McKinnon, B. I. Graubard, and R. P. Troiano, ‘‘Prevalence and trends in physical activity among older adults in the United
States: A comparison across three national surveys,’’ Preventive Med., vol. 89, pp. 37–43, Aug. 2016.
7. A. C. Morrill and C. D. Chinn, ‘‘The obesity epidemic in the United States,’’ J. Public Health Policy, vol. 25, nos. 3–4, pp. 353–366, Dec.
2004, doi: 10.1057/palgrave.jphp.3190035.
8. Estimation of Obesity Levels Based on Eating Habits and Physical Condition, UCI Machine Learning Repository, Irvine, CA, USA, 2019,
doi: 10.24432/C5H31Z.
9. F. M. Palechor and A. D. L. H. Manotas, ‘‘Dataset for estimation of obesity levels based on eating habits and physical condition in
individuals from colombia, Peru and Mexico,’’ Data Brief, vol. 25, Aug. 2019, Art. no. 104344.
10. G. Shao, ‘‘Comparison of prediction of obesity status based on different machine learning approaches with different factor quantities,’’ in
Proc. Int. Conf. Biomed. Intell. Syst. (IC-BIS), Dec. 2022, p. 144.
11. I. G. S. M. Diayasa, M. Idhom, A. Fauzi, and A. T. Damaliana, ‘‘Stacking ensemble methods to predict obesity levels in adults,’’ in Proc.
IEEE 8th Inf. Technol. Int. Seminar (ITIS), Oct. 2022, pp. 339–344
12. Z. Zheng and K. Ruggiero, ‘‘Using machine learning to predict obesity in high school students,’’ in Proc. IEEE Int. Conf. Bioinf. Biomed.
(BIBM), Nov. 2017, pp. 2132–2138.
13. H. B. Kibria, M. Nahiduzzaman, M. O. F. Goni, M. Ahsan, and J. Haider, ‘‘An ensemble approach for the prediction of diabetes mellitus
using a soft voting classifier with an explainable AI,’’ Sensors, vol. 22, no. 19, p. 7268, Sep. 2022.
14. M. J. Raihan, M. A. M. Khan, S. H. Kee, and A. A. Nahid, ‘‘Detection of the chronic kidney disease using XGBoost classifier and
explaining the influence of the attributes on the model using SHAP,’’ Sci. Rep., vol. 13, no. 1, p. 6263, Apr. 2023, doi: 10.1038/s41598-
023-33525-0.
15. S. Jahan, K. A. Taher, M. S. Kaiser, M. Mahmud, M. S. Rahman, A. S. M. S. Hosen, and I. Ra, ‘‘Explainable AI-based Alzheimer’s
prediction and management using multimodal data,’’ PLoS ONE, vol. 18, Nov. 2023, Art. no. 0294253.
16. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees. Monterey, CA, USA: Wadsworth and
Brooks, 1984
17. Sharma, A., and S. Gupta (2022). “An ensemble approach for predicting obesity based on lifestyle and demographic factors.” DOI:
10.1155/2022/4549320; Journal of Healthcare Engineering, 2022, 1 - 10.
18. Ameen, S., & Hossain, M. M (2022). “A machine learning approach for obesity prediction among adolescents: A case study in Bangladesh.”
DOI: 10.3390/healthcare10040705. Healthcare, 10 (4) 705.
19. Tran, D. T., & Le, T. N (2023). “Ensemble learning for predicting obesity risk: A case study of Vietnamese adolescents.” 23 (1), 1 - 11.
doi: 10.1186/s12889-023-16273-5. BMC Public Health.
20. In 2023, Alhassan, S. I., and Majid, M. A published “Obesity prediction using ensemble machine learning techniques: A case study of adult
populations.” 29 (1): 146–157 in the Health Informatics Journal; DOI: 10.1177/14604582211057523.
21. In 2023, A. Subramanian and P. Karthikeyan published “Machine learning in healthcare: A comprehensive review of the applicati ons in
obesity prediction.” Medical Artificial Intelligence, 127, 102914, 10.1016/j.artmed.2022.102914
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