This web page was created programmatically, to learn the article in its unique location you may go to the hyperlink bellow:
https://www.nature.com/articles/s41598-025-20936-4
and if you wish to take away this text from our website please contact us
World Health Organization. Obesity and chubby, 1 March 2024. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight. [Accessed 5 April 2024].
Yao, Z. et al. Associations between class I, II, or III weight problems and well being outcomes. NEJM Evidence, 4 (4), EVIDoa2400229 (2025).
Ferreras, A. et al. and I. d. l. T. Díez. Systematic assessment of machine studying utilized to the prediction of weight problems and chubby, J. Med. Syst. 47 (8), (2023).
DeGregory, Okay. W. et al. Rev. Mach. Learn. Obes. Obesity Reviews, 19, 5, 668–685, (2018).
Ganie, S. M., Pramanik, P. Okay. D., Mallik, S. & Zhao, Z. Chronic kidney illness prediction utilizing boosting strategies primarily based on medical parameters. PLoS ONE. 18 (12), e0295234 (2023).
Rautiainen, I. & Äyrämö, S. Predicting chubby and weight problems in later life from childhood information: A assessment of predictive modeling approaches. In Computational Sciences and Artificial Intelligence in Industry. Intelligent Systems, Control and Automation: Science and Engineering Vol. 76 (eds Tuovinen, T. et al.) 203–220 (Springer, 2022).
Safaei, M., Sundararajan, E. A., Driss, M., Boulila, W. & Shapi’i, A. A scientific literature assessment on weight problems: Understanding the causes & penalties of weight problems and reviewing varied machine studying approaches used to foretell weight problems. Comput. Biol. Med. 136, 104754 (2021).
Ganie, S. M., Pramanik, P. Okay. D., Malik, M. B., Mallik, S. & Qin, H. An ensemble studying strategy for diabetes prediction utilizing boosting strategies. Frontiers Genetics, 14, (2023).
Ganie, S. M., Pramanik, P. Okay. D., Malik, M. B., Nayyar, A. & Kwak, Okay. S. An improved ensemble studying strategy for coronary heart illness prediction utilizing boosting algorithms. Comput. Syst. Sci. Eng. 46 (3), 3993–4006 (2023).
Ganie, S. M., Pramanik, P. Okay. D. & Zhao, Z. Ensemble studying with explainable AI for improved coronary heart illness prediction primarily based on a number of datasets. Sci. Rep. 15, 13912 (2025).
Mienye, I. D. & Sun, Y. A survey of ensemble studying: Concepts, Algorithms, Applications, and prospects. IEEE Access. 10, 99129–99149 (2022).
Ganie, S. M. & Pramanik, P. Okay. D. A comparative evaluation of boosting algorithms for power liver, Healthcare Analytics, 5 (100313), (2024).
Ganie, S. M. & Pramanik, P. Okay. D. Interpretable lung most cancers danger prediction utilizing ensemble studying and XAI primarily based on way of life and demographic information. Comput. Biol. Chem. 117, 108438 (2025).
Dutta, R. R., Mukherjee, I. & Chakraborty, C. Obesity illness danger prediction utilizing machine studying. Int. J. Data Sci. Analytics, 19, 709–718 (2025).
Osadchiy, V. et al. Machine studying mannequin to foretell weight problems utilizing intestine metabolite and mind microstructure information. Sci. Rep. 13, 5488 (2023).
Kaur, R., Kumar, R. & Gupta, M. Predicting danger of weight problems and meal planning to scale back the overweight in maturity utilizing synthetic intelligence, Endocrine, 78, 458–469, (2022).
Ferdowsy, F., Rahi, Okay. S. A., Jabiullah, M. I. & Habib, M. T. A machine studying strategy for weight problems danger prediction. Current Res. Behav. Sci. 2 (100053), (2021).
Maria, A. S., Sunder, R. & Kumar, R. S. Obesity Risk Prediction Using Machine Learning Approach, in International Conference on Networking and Communications (ICNWC), Chennai, India, (2023).
Jindal, Okay., Baliyan, N. & Rana, P. S. Obesity prediction utilizing ensemble machine studying approaches. In Recent Findings Intel. Comput. Tech. Adv. Intel. Syst. Comput. (eds.,). 708 355–362 (Springer, 2018).
Khodadadi, N., Saber, M. & Abotaleb, M. A Data-Driven strategy for weight problems classification utilizing machine studying. J. Artif. Intell. Metaheuristics. 3 (2), 08–17 (2023).
Bag, H. G. G. et al. Estimation of Obesity Levels via the Proposed Predictive Approach Based on Physical Activity and Nutritional Habits, Diagnostics, 13 (18), 2949, (2023).
Singh, B. & Tawfik, H. Machine studying strategy for the early prediction of the danger of chubby and weight problems in younger folks. In Computational Science (ICCS 2020). Lecture Notes in Computer Science Vol. 12140 (eds Krzhizhanovskaya, V. V. et al.) 523–535 (Springer, 2020).
Lim, H., Lee, H. & Kim, J. A prediction mannequin for childhood weight problems danger utilizing the machine studying methodology: a panel examine on Korean youngsters, Scientific Reports, 13 (10122), (2023).
Thamrin, S. A., Arsyad, D. S., Kuswanto, H., Lawi, A. & Nasir, S. Predicting weight problems in adults utilizing machine studying strategies: an evaluation of Indonesian primary well being analysis 2018. Frontiers Nutrition, 8, 669155 (2021).
Pang, X., Forrest, C. B., Lê-Scherban, F. & Masino, A. J. Prediction of early childhood weight problems with machine studying and digital well being file information. International J. Med. Informatics, 150 (104454), (2021).
Jeon, J., Lee, S. & Oh, C. Age-specific danger elements for the prediction of weight problems utilizing a machine studying strategy. Front. Public. Health. 10, 998782 (2022).
Khater, T., Tawfik, H. & Singh, B. Machine Learning for the Classification of Obesity Levels Based on Lifestyle Factors, in seventh Inter. Conference on Cloud and Big Data Comput. Manc. UK, (2023).
Rodríguez, E., Rodrígueza, E., Nascimento, L., Silvaa, A. & Marins, F. Machine studying strategies to foretell chubby or weight problems, in 4th International Conference on Informatics & Data-Driven Medicine, Valencia, Spain, (2021).
Jeon, S., Kim, M., Yoon, J., Lee, S. & Youm, S. Machine learning-based weight problems classification contemplating 3D physique scanner measurements. Sci. Rep. 13, 3299 (2023). (Article quantity.
Suresh, C. et al. Obesity Prediction Based on Daily Lifestyle Habits and Other Factors Using Different Machine Learning Algorithms, in Proceedings of Second International Conference on Advances in Computer Engineering and Communication Systems. Algorithms for Intelligent Systems, A. B. Reddy, B. Kiranmayee, R. Mukkamala and Okay. Srujan Raju, Eds., Springer, Singapore, pp. 397–407. (2022).
Diayasa, I. G. S. M., Idhom, M., Fauzi, A. & Damaliana, A. T. Stacking Ensemble Methods to Predict Obesity Levels in Adults, in eighth Information Technology International Seminar (ITIS), Surabaya, Indonesia, (2022).
Solomon, D. D. et al. Hybrid Majority Voting: Prediction and Classification Model for Obesity, Diagnostics, 13 (15), 2610, (2023).
Choudhuri, A. A hybrid machine studying mannequin for Estimation of weight problems ranges. In Data Management, Analytics and Innovation. ICDMAI 2022. Lecture Notes on Data Engineering and Communications Technologies Vol. 137 (eds Goswami, S. et al.) 315–329 (Springer, 2023).
Ganie, S. M., Reddy, B. B., Hemachandran, Okay. & Rege, M. An investigation of ensemble studying strategies for weight problems danger prediction utilizing way of life information. Decis. Analytics J. 14, 100539 (2025).
Lin, W., Shi, S., Huang, H., Wen, J. & Chen, G. Predicting danger of weight problems in chubby adults utilizing interpretable machine studying algorithms. Frontiers Endocrinology, 14, (2023).
Zhou, Z. H. Ensemble Methods: Foundations and Algorithms (Chapman & Hall/CRC, 2012).
Sarmah, U., Borah, P. & Bhattacharyya, D. Okay. Ensemble studying strategies: an empirical examine. SN Comput. Sci. 5, 924 (2024).
Liu, Z. Ensemble studying. In Artificial Intelligence for Engineers 221–242 (Springer, 2025).
Freund, Y. & Schapire, R. E. A Decision-Theoretic generalization of On-Line studying and an software to boosting. J. Comput. Syst. Sci. 55 (1), 119–139 (1997).
Breiman, L. Bagging predictors. Maching Learn. 24, 123–140 (1996).
Breiman, L. Random forests. Mach. Learn. 45 (1), 5–32 (2001).
Geurts, P., Ernst, D. & Wehenkel, L. Extremely randomized bushes. Mach. Learn. 63, 3–42 (2006).
Zhao, C. et al. EnhanceTree and BoostForest for ensemble studying. IEEE Trans. Pattern Anal. Mach. Intell. 45 (7), 8110–8126 (2023).
Aziz, N. et al. A examine on gradient boosting algorithms for improvement of AI monitoring and prediction methods, in Inter. Confer. Comput. Intel. (ICCI), Malaysia, (2020).
Chen, T. & Guestrin, C. XGBoost: A scalable and transportable parallel tree boosting framework, in twenty second ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, SanFrancisco, USA, (2016).
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V. & Gulin, A. CatBoost: A strong and environment friendly categorical characteristic boosting resolution tree, Advances in Neural Infor. Proc. Syst. (NeurIPS 2018), 31, 6237–6249, (2018).
Ke, G. et al. LightGBM: A Highly Efficient Gradient Boosting Decision Tree, Advances in Neural Information Processing Systems (NIPS 2017), 30, 3146–3154, (2017).
Freund, Y. & Schapire, R. E. AShort introduction to boosting. J. Japanese Soc. Artif. Intell. 14 (5), 771–780 (1999).
Zhang, Y., Zhang, H., Cai, J. & Yang, B. A Weighted voting classifier primarily based on differential evolution, Abstract and Applied Analysis, 2014, 376950, 2014. (2014).
Giraud-Carrier, C. Combining Base-Learners into ensembles. In Metalearning. Cognitive Technologies (eds. Brazdil, P., Jan N. van Rijn, Soares, C., Vanschoren, J.) 169–188 (Springer, 2022).
Ganie, S. M., Pramanik, P. Okay. D. & Zhao, Z. Enhanced and interpretable prediction of a number of most cancers varieties utilizing a stacking ensemble strategy with SHAP Analysis, Bioengineering, 15, 472, (2025).
Hastie, T., Tibshirani, R. & Friedman, J. The parts of statistical studying: information mining, inference, and prediction (Springer-, 2009).
Cover, T. & Hart, P. E. Nearest neighbor sample classification. IEEE Trans. Inf. Theory. 13 (1), 21–27 (1967).
Murtagh, F. Multilayer perceptrons for classification and regression, Neurocomputing, 2 (5–6), 183–197, (1991).
McCallum, A. & Nigam, Okay. A comparability of occasion fashions for naive bayes textual content classification, AAAI-98 workshop on studying for textual content categorization, 752 (1), 41–48, (1998).
Schapire, R. E. & Singer, Y. Improved boosting algorithms utilizing Confidence-rated predictions. Mach. Learn. 37, 297–336 (1999).
Palechor, F. M. & Manotas, A. Dataset for estimation of weight problems ranges primarily based on consuming habits and bodily situation in people from Colombia, Peru and Mexico, Data in Brief, 25 (104344), (2019).
Hodges, J. L. Jr. & Lehmann, E. L. Rank strategies for mixture of impartial experiments in evaluation of variance. In Selected Works of E. L. Lehmann. Selected Works in Probability and Statistics (ed. Rojo, J.) 403–418 (Springer, 2012).
Holm, S. A easy sequentially rejective a number of check process. Scand. J. Stat. 6 (2), 65–70 (1979).
Nauta, M. et al. From anecdotal proof to quantitative analysis strategies: a scientific assessment on evaluating explainable AI, ACM Comput. Surveys, 55 (13s), 1–42, (2023).
Lundberg, S. M. & Lee, S. I. A unified strategy to decoding mannequin predictions, in thirty first Inter. Conf. Neural Infor. Proc. Syst. (NIPS’17), Long Beach, California, (2017).
Ribeiro, M. T., Singh, S. & Guestrin, C. Why Should I Trust You? Explaining the Predictions of Any Classifier, in twenty second ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ‘16), San Francisco, California, (2016).
This web page was created programmatically, to learn the article in its unique location you may go to the hyperlink bellow:
https://www.nature.com/articles/s41598-025-20936-4
and if you wish to take away this text from our website please contact us
This web page was created programmatically, to learn the article in its unique location you…
This web page was created programmatically, to learn the article in its unique location you…
This web page was created programmatically, to learn the article in its authentic location you…
This web page was created programmatically, to learn the article in its unique location you'll…
This web page was created programmatically, to learn the article in its authentic location you'll…