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Endocrine

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10.06.2024 | Original Article

Predicting the onset of overweight in Chinese high school students: a machine-learning approach in a one-year prospective cohort study

verfasst von: Zikang Zhang, Wei Peng, Shaoming Sun, Jianguo Ma, Yining Sun, Fangwen Zhang

Erschienen in: Endocrine | Ausgabe 2/2024

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Abstract

Objective

This study aimed to develop and evaluate machine-learning models for predicting the onset of overweight in adolescents aged 14‒17, utilizing easily collectible personal information.

Methods

This study was a one-year prospective cohort study. Baseline data were collected through anthropometric measurements and questionnaires, and the incidence of overweight was calculated one year later via anthropometric measurements. Predictive factors were selected through univariate analysis. Six machine-learning models were developed for predicting the onset of overweight. The SHapley Additive exPlanations (SHAP) was used for global and local interpretation of the models.

Results

Out of 1,241 adolescents, 204 (16.4%) were identified as overweight after one year. Nineteen features were associated with the overweight incidence in univariable analysis. Participants were randomly divided into a training group and a testing group in a 7:3 ratio. The Light Gradient Boosting Machine (LGBM) algorithm achieved outperformed other models, achieving the following metrics: Accuracy (0.956), Recall (0.812), Specificity (0.983), F1-score (0.855), AUC (0.961). Importance ranking revealed that the top 11 minimal feature set can maintain the stability of model performance.

Conclusions

The onset of overweight in adolescents was accurately predicted using easily collectible personal information. The LGBM-based model exhibited superior performance. Oversampling technique notably improved model performance. The model interpretation technique provided innovative strategies for managing adolescent overweight/obesity.

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)32129-3CrossRef

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.0253-9624.2017.04.005CrossRef

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)00118-2CrossRef

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.2023.1128552CrossRefPubMedPubMedCentral

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/S1368980021003190CrossRefPubMedPubMedCentral

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/s41598-020-61719-3CrossRefPubMedPubMedCentral

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/10.1159/000519268CrossRefPubMed

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/10.1177/0890117120984943CrossRefPubMedPubMedCentral

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.0130218CrossRefPubMedPubMedCentral

10.

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.2016.08.011CrossRefPubMedPubMedCentral

11.

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/S1368980021000690CrossRefPubMedPubMedCentral

12.

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.2021.02.010CrossRefPubMed

13.

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/s41366-023-01356-8CrossRef

14.

G. Colmenarejo, Machine Learning Models to Predict Childhood and Adolescent Obesity: A Review. Nutrients 12, 2466 (2020). https://doi.org/10.3390/nu12082466CrossRefPubMedPubMedCentral

15.

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.2021.104754CrossRefPubMed

16.

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.0215571CrossRefPubMedPubMedCentral

17.

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/ijerph18083966CrossRefPubMedPubMedCentral

18.

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.0249285CrossRefPubMedPubMedCentral

19.

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.2020.637434CrossRef

20.

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

21.

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)

22.

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)

23.

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/s12020-022-03072-1CrossRefPubMed

24.

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.953CrossRef

25.

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

26.

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.12647CrossRefPubMedPubMedCentral

27.

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.2020.10.016CrossRef

28.

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.2023.1090146CrossRefPubMedPubMedCentral

29.

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/s41598-021-81205-8CrossRefPubMedPubMedCentral

30.

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.2021.104454CrossRef

31.

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

32.

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/s12938-023-01109-8CrossRefPubMedPubMedCentral

33.

B. Zhou, B.J. Bartholmai, S. Kalra, T. Osborn, X. Zhang, Lung mass density prediction using machine learning based on ultrasound surface wave elastography and pulmonary function testing. J. Acoust. Soc. Am. 149, 1318 (2021). https://doi.org/10.1121/10.0003575CrossRefPubMedPubMedCentral

34.

M.K. Uçar, Z. Uçar, F. Köksal, N. Daldal, Estimation of body fat percentage using hybrid machine learning algorithms. Measurement. 167, (2021). https://doi.org/10.1016/j.measurement.2020.108173

Titel: Predicting the onset of overweight in Chinese high school students: a machine-learning approach in a one-year prospective cohort study
verfasst von: Zikang Zhang
Wei Peng
Shaoming Sun
Jianguo Ma
Yining Sun
Fangwen Zhang
Publikationsdatum: 10.06.2024
Verlag: Springer US
Erschienen in: Endocrine / Ausgabe 2/2024
Print ISSN: 1355-008X
Elektronische ISSN: 1559-0100
DOI: https://doi.org/10.1007/s12020-024-03902-4

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