Collaboration, N.C.D.R.F., Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults. Lancet 390, 2627–2642 (2017). https://doi.org/10.1016/S0140-6736(17)32129-3
S. Wang, Y.H. Dong, Z.H. Wang, Z.Y. Zou, J. Ma, Trends in overweight and obesity among Chinese children of 7-18 years old during 1985-2014. Chin. J. Prevent. Med. 51, 300–305 (2017). https://doi.org/10.3760/cma.j.issn.0253-9624.2017.04.005
Y. Wang, L. Zhao, L. Gao, A. Pan, H. Xue, Health policy and public health implications of obesity in China. Lancet Diab. Endocrinol. 9, 446–461 (2021). https://doi.org/10.1016/S2213-8587(21)00118-2
C. Li, M. Zhang, A.Y. Tarken, Y. Cao, Q. Li, H. Wang, Secular trends and sociodemographic determinants of thinness, overweight and obesity among Chinese children and adolescents aged 7-18 years from 2010 to 2018. Front. Public Health 11, 1128552 (2023). https://doi.org/10.3389/fpubh.2023.1128552
Article PubMed PubMed Central Google Scholar
M.S. Mohamad, B. Mahadir Naidu, R. Kaltiala, S.M. Virtanen, S. Lehtinen-Jacks, Thinness, overweight and obesity among 6- to 17-year-old Malaysians: secular trends and sociodemographic determinants from 2006 to 2015. Public Health Nutr. 24, 6309–6322 (2021). https://doi.org/10.1017/S1368980021003190
Article PubMed PubMed Central Google Scholar
T.M. Schnurr, C.S. Morgen, D. Borisevich, R.N. Beaumont, L. Engelbrechtsen, L. Angquist et al. The influence of transmitted and non-transmitted parental BMI-associated alleles on the risk of overweight in childhood. Sci. Rep. 10, 4806 (2020). https://doi.org/10.1038/s41598-020-61719-3
Article CAS PubMed PubMed Central Google Scholar
C. Ding, J. Fan, F. Yuan, G. Feng, W. Gong, C. Song et al. Association between Physical Activity, Sedentary Behaviors, Sleep, Diet, and Adiposity among Children and Adolescents in China. Obes. Facts 15, 26–35 (2022). https://doi.org/10.1159/000519268
Article CAS PubMed Google Scholar
C.M. Bejarano, J.A. Carlson, T.L. Conway, B.E. Saelens, K. Glanz, S.C. Couch et al. Physical Activity, Sedentary Time, and Diet as Mediators of the Association Between TV Time and BMI in Youth. Am. J. Health Promot. 35, 613–623 (2021). https://doi.org/10.1177/0890117120984943
Article PubMed PubMed Central Google Scholar
B. Sartorius, L.J. Veerman, M. Manyema, L. Chola, K. Hofman, Determinants of Obesity and Associated Population Attributability, South Africa: Empirical Evidence from a National Panel Survey, 2008-2012. PloS One 10, e0130218 (2015). https://doi.org/10.1371/journal.pone.0130218
Article CAS PubMed PubMed Central Google Scholar
L. Cai, T. Zhang, J. Ma, L. Ma, J. Jing, Y. Chen, Self-perception of weight status and its association with weight-related knowledge, attitudes, and behaviors among Chinese children in Guangzhou. J. Epidemiol. 27, 338–345 (2017). https://doi.org/10.1016/j.je.2016.08.011
Article PubMed PubMed Central Google Scholar
M.C. San Martini, D. de Assumpcao, M.B.A. Barros, A.A. Barros Filho, J. Mattei, Weight self-perception in adolescents: evidence from a population-based study. Public Health Nutr. 24, 1648–1656 (2021). https://doi.org/10.1017/S1368980021000690
Article PubMed PubMed Central Google Scholar
H. Rossman, S. Shilo, S. Barbash-Hazan, N.S. Artzi, E. Hadar, R.D. Balicer et al. Prediction of Childhood Obesity from Nationwide Health Records. J. Pediatr. 233, 132–140.e131 (2021). https://doi.org/10.1016/j.jpeds.2021.02.010
N. Ziauddeen, P.J. Roderick, G. Santorelli, N.A. Alwan, Prediction of childhood overweight and obesity at age 10-11: findings from the Studying Lifecourse Obesity PrEdictors and the Born in Bradford cohorts. Int J. Obes. 47, 1065–1073 (2023). https://doi.org/10.1038/s41366-023-01356-8
G. Colmenarejo, Machine Learning Models to Predict Childhood and Adolescent Obesity: A Review. Nutrients 12, 2466 (2020). https://doi.org/10.3390/nu12082466
Article PubMed PubMed Central Google Scholar
M. Safaei, E.A. Sundararajan, M. Driss, W. Boulila, A. Shapi’i, A systematic literature review on obesity: Understanding the causes & consequences of obesity and reviewing various machine learning approaches used to predict obesity. Comput. Biol. Med. 136, 104754 (2021). https://doi.org/10.1016/j.compbiomed.2021.104754
R. Hammond, R. Athanasiadou, S. Curado, Y. Aphinyanaphongs, C. Abrams, M.J. Messito et al. Predicting childhood obesity using electronic health records and publicly available data. PloS One 14, e0215571 (2019). https://doi.org/10.1371/journal.pone.0215571
Article CAS PubMed PubMed Central Google Scholar
X. Cheng, S.-y Lin, J. Liu, S. Liu, J. Zhang, P. Nie et al. Does Physical Activity Predict Obesity—A Machine Learning and Statistical Method-Based Analysis. Int. J. Environ. Res. Public Health 18, 3966 (2021). https://doi.org/10.3390/ijerph18083966
Article PubMed PubMed Central Google Scholar
L. Yu, A. Halalau, B. Dalal, A.E. Abbas, F. Ivascu, M. Amin et al. Machine learning methods to predict mechanical ventilation and mortality in patients with COVID-19. PloS One 16, e0249285 (2021). https://doi.org/10.1371/journal.pone.0249285
Article CAS PubMed PubMed Central Google Scholar
Q.Y. Zhao, L.P. Liu, J.C. Luo, Y.W. Luo, H. Wang, Y.J. Zhang et al. A Machine-Learning Approach for Dynamic Prediction of Sepsis-Induced Coagulopathy in Critically Ill Patients With Sepsis. Front. Med. 7, 637434 (2020). https://doi.org/10.3389/fmed.2020.637434
J. Jeon, S. Lee, C. Oh. Age-specific risk factors for the prediction of obesity using a machine learning approach. Front. Public Health. 10, (2023). https://doi.org/10.3389/fpubh.2022.998782
Group of China Obesity Task Force, Body mass index reference norm for screening overweight and obesity in Chinese children and adolescents. Zhonghua Liu Xing Bing. Xue Za Zhi 25, 97–102 (2004)
C. Chen, F.C. Lu, Department of Disease Control Ministry of Health, PR China, The guidelines for prevention and control of overweight and obesity in Chinese adults. Biomed. Environ. Sci. BES 17, 1–36 (2004)
Q. Wang, M. Yang, B. Pang, M. Xue, Y. Zhang, Z. Zhang et al. Predicting risk of overweight or obesity in Chinese preschool-aged children using artificial intelligence techniques. Endocrine 77, 63–72 (2022). https://doi.org/10.1007/s12020-022-03072-1
Article CAS PubMed Google Scholar
N.V. Chawla, K.W. Bowyer, L.O. Hall, W.P. Kegelmeyer, SMOTE: synthetic minority over-sampling technique. J. Artif. Int. Res. 16, 321–357 (2002). https://doi.org/10.1613/jair.953
S.M. Lundberg, I.S. Lee, A unified approach to interpreting model predictions. In Proceedings of the 31st International Conference on Neural Information Processing Systems. 4768–4777 (2017). https://doi.org/10.48550/arXiv.1705.07874
M. Welten, A.H. Wijga, M. Hamoen, U. Gehring, G.H. Koppelman, J.W.R. Twisk et al. Dynamic prediction model to identify young children at high risk of future overweight: Development and internal validation in a cohort study. Pediatr. Obes. 15, e12647 (2020). https://doi.org/10.1111/ijpo.12647
Article PubMed PubMed Central Google Scholar
S. Zare, M.R. Thomsen, R.M. Nayga Jr., A. Goudie, Use of Machine Learning to Determine the Information Value of a BMI Screening Program. Am. J. Prevent. Med. 60, 425–433 (2021). https://doi.org/10.1016/j.amepre.2020.10.016
K. Fujihara, M. Yamada Harada, C. Horikawa, M. Iwanaga, H. Tanaka, H. Nomura et al. Machine learning approach to predict body weight in adults. Front Public Health 11, 1090146 (2023). https://doi.org/10.3389/fpubh.2023.1090146
Article PubMed PubMed Central Google Scholar
H. Marcos-Pasero, G. Colmenarejo, E. Aguilar-Aguilar, A. Ramirez de Molina, G. Reglero, V. Loria-Kohen, Ranking of a wide multidomain set of predictor variables of children obesity by machine learning variable importance techniques. Sci. Rep. 11, 1910 (2021). https://doi.org/10.1038/s41598-021-81205-8
Article CAS PubMed PubMed Central Google Scholar
X. Pang, C.B. Forrest, F. Le-Scherban, A.J. Masino, Prediction of early childhood obesity with machine learning and electronic health record data. Int J. Med Inf. 150, 104454 (2021). https://doi.org/10.1016/j.ijmedinf.2021.104454
Z.Y. Zheng, K. Ruggiero, Using Machine Learning to Predict Obesity in High School Students. In: Biological Ontologies and Knowledge Bases Workshop at IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM). 2132–2138 (2017). https://doi.org/10.1109/BIBM.2017.8217988
W. Peng, F. Wang, S. Sun, Y. Sun, J. Chen, M. Wang, Does multidimensional daily information predict the onset of myopia? A 1-year prospective cohort study. Biomed. Eng. Online 22, 45 (2023). https://doi.org/10.1186/s12938-023-01109-8
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