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Prevalence of Fatty Liver among Children under Multiple Machine Learning Models
Yunlong Lu1, Wenyu Li1, Xiangbo Gong1
1From the School of Mathematics and Statistics, Beihua University, Jilin, China, and the Departments of Mathematics and Physics and Biology and Chemistry, Texas A&M International University, Laredo, Texas.
Insights
Machine learning models identified key factors for childhood fatty liver disease in South Texas. Higher body mass index in children is strongly associated with an increased probability of developing this condition.
Area of Science:
- Pediatric Gastroenterology
- Medical Informatics
- Biostatistics
Background:
- Childhood fatty liver disease is a growing concern.
- Ultrasound data analysis is crucial for diagnosis.
- Machine learning offers novel approaches for disease prediction.
Purpose of the Study:
- To identify factors contributing to fatty liver in children using ultrasound data.
- To develop machine learning models for predicting childhood fatty liver.
- To inform prevention and treatment strategies for pediatric fatty liver disease.
Main Methods:
- Utilized the CatBoost algorithm for feature selection.
- Employed grid search for parameter optimization.
- Developed and evaluated binary classification models (logistic regression, CatBoost) for fatty liver prediction in obese children.
- Compared model performance using AUC, precision, accuracy, recall, and F1 score.
Main Results:
- Selected features included body mass index, height, liver size, kidney volume, glomerular filtration rate, and liver diameter.
- Higher body mass index correlated with increased fatty liver probability in children.
- Machine learning models demonstrated predictive capabilities for fatty liver in obese children.
Conclusions:
- Logistic regression and CatBoost models predict a high probability of fatty liver in severely obese children (74.47%-92.22%) and obese children (73.45%-85.41%).
- Boys showed a slightly higher mean probability of fatty liver compared to girls (3.00%-3.95% difference).
- Machine learning models provide valuable insights into childhood fatty liver risk factors and prevalence.
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%.
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