Original Article

Prevalence of Fatty Liver among Children under Multiple Machine Learning Models

Authors: Yunlong Lu, PhD, Wenyu Li, PhD, Xiangbo Gong, MS, Jing Mi, MS, Hongwei Wang, PhD, Fernando G. Quintana, PhD


Objectives: To analyze the possible factors causing fatty liver in children based on ultrasound data of children in south Texas, and to establish machine learning models of fatty liver in children to provide ideas for the prevention and treatment of fatty liver in children.

Methods: The binary classification model of fatty liver problem in obese children in Texas was established under the multiple model. First, we selected important features using the CatBoost algorithm. Second, the best parameters of the algorithm were selected on the training set and the validation set by using the grid search method, and all six models were tested on the test set. The six models then were compared by area under the curve value, precision, accuracy, recall rate, and F1 score in a model evaluation. Then, two algorithms, logic regression and CatBoost, were selected to establish prediction models of fatty liver disease in children.

Results: We selected body mass index, height, liver size, kidney volume, glomerular filtration rate, and liver diameter as the features used in the machine learning model. The prediction models we chose showed that children with higher body mass index at the same age tended to have a greater probability of fatty liver.

Conclusions: Based on the analysis of the results of the two prediction models established by logistic regression and CatBoost, we determined that the mean probability of fatty liver in severely obese children was between 74.47% and 92.22%, 73.45% and 85.41% in obese children, and slightly higher in boys than in girls, with a mean difference of 3.00% to 3.95%.
Posted in: Liver Disease5

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1. Stocker R, Yamamoto Y, McDonagh A, et al. Bilirubin is an antioxidant of possible physiological importance. Science 1987;235:1043–1046.
2. Gu DF, Reynolds K, Wu XG, et al. Prevalence of the metabolic syndrome and overweight among adults in China. Lancet 2005;365:1398–1405.
3. Kumar S, Kelly AS. Review of childhood obesity: from epidemiology, etiology, and comorbidities to clinical assessment and treatment. Mayo Clin Proc 2017;92:251–265.
4. Rankin J, Matthews L, Cobley S, et al. Psychological consequences of childhood obesity: psychiatric comorbidity and prevention. Adolesc Health Med Ther 2016;7:125–146.
5. Le MH, Devaki P, Ha NB, et al. Prevalence of non-alcoholic fatty liver disease and risk factors for advanced fibrosis and mortality in the United States. PloS One 2017;12:e0173499.
6. Sorof JM, Lai D, Turner J, et al. Overweight, ethnicity, and the prevalence of hypertension in school-aged children. Pediatrics 2004;113:475–482.
7. Lazo M, Clark JM. The epidemiology of nonalcoholic fatty liver disease: a global perspective. Sem Liver Dis 2008;28:339–350.
8. Widhalm K, Ghods E. Nonalcoholic fatty liver diseases: a challenge for pediatricians. Int J Obes (Lond) 2010;34:1451–1467.
9. Schwimmer JB, Deutsch R, Kahen T, et al. Prevalence of fatty liver in children and adolescents. Pediatrics 2006;118:1388–1393.
10. Ogden CL, Carroll MD, Kit BK, et al. Prevalence of obesity and trends in body mass index among US children and adolescents, 1999-2010. JAMA 2012;307:483–490.
11. Ogden CL, Carroll MD, Kit BK, et al. Prevalence of obesity in the United States, 2009-2010. NCHS Data Brief 2012;(82):1–8.
12. Ogden CL, Carroll MD, Kit BK, et al. Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA 2014;311:806–814.
13. Wu C-C, Yeh W-C, Hsu WD, et al. Prediction of fatty liver disease using machine learning algorithms. Comput Methods Programs Biomed 2019; 170:23–29.
14. Fu C-C, Chen M-C, Li Y-M, et al. The risk factors for ultrasound-diagnosed non-alcoholic fatty liver disease among adolescents. Ann Acad Med Singap 2009;38:15–17.
15. Skinner AC, Skelton JA. Prevalence and trends in obesity and severe obesity among children in the United States, 1999-2012. JAMA Pediatr 2014;168: 561–566.
16. Reifsnider E, Jeong M, Chatterjee P. An ecological approach to obesity in Mexican American children. J Pediatr Health Care 2020;34:212–221.
17. Wang H, Quintana FG, Lu Y, et al. An application of ordinal logistic regression model to a health survey in a Hispanic university. 2021;doi: 10. 13140/RG.2.2.34319.30887.
18. Zhaoyi L. A study on the effects of different forms of fitness on the physical health of the elderly. Harbin, China: Harbin Institute of Physical Education; 2016.
19. Xiong CM, Guo H, Wu Y. A review of research on missing data processing methods. Comp Eng Appl 2021;57:27–38.
20. Yang X. A review of performance metrics for classification learning algorithms. (in Chinese). Comp Sci. 2021;48:209–219.
21. Cai J, Luo J, Wang S, et al. Feature selection in machine learning: a new perspective. Neurocomputing 2018;300:70–79.
22. Wang L, Wang T, Xiao W, et al. Catboost-based feature selection algorithm (in Chinese). J Changchun Univ Technol 2021;42:34–39.
23. Shaunak M, Byrne CD, Davis N, et al. Non-alcoholic fatty liver disease and childhood obesity. Arch Dis Child 2021;106:3–8.