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Prediction of early childhood obesity with machine learning and electronic health record data
Xueqin Pang1, Christopher B Forrest2, Félice Lê-Scherban3
1Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, USA.
Insights
Machine learning models predict childhood obesity using electronic health records. XGBoost demonstrated superior performance, outperforming other models in predicting obesity up to age seven.
Area of Science:
- Pediatric Health
- Machine Learning
- Public Health
Background:
- Childhood obesity is a significant public health concern with long-term health implications.
- Early identification and intervention are crucial for managing childhood obesity.
- Electronic Healthcare Record (EHR) data offers a rich resource for developing predictive models.
Purpose of the Study:
- To develop and compare seven machine learning models for predicting childhood obesity.
- To utilize EHR data up to age two years for predicting obesity incidence by age seven.
- To identify the most effective machine learning model for early childhood obesity prediction.
Main Methods:
- Utilized EHR data from 27,203 pediatric patients.
- Developed seven distinct machine learning models to predict obesity incidence (BMI > 95th percentile).
- Evaluated model performance using standard classifier metrics and statistical comparison tests.
Main Results:
- The XGBoost model achieved the highest performance with an AUC of 0.81.
- XGBoost significantly outperformed other models in precision, F1-score, accuracy, and specificity.
- Sensitivity was maintained at 80% across models for fair comparison.
Conclusions:
- Machine learning models can effectively predict childhood obesity using early EHR data.
- The XGBoost model shows significant promise for early identification of at-risk children.
- The developed workflow is adaptable for creating other clinical prediction models from EHR data.
Objective:
This study compares seven machine learning models developed to predict childhood obesity from age > 2 to ≤ 7 years using Electronic Healthcare Record (EHR) data up to age 2 years.
Materials And Methods:
EHR data from of 860,510 patients with 11,194,579 healthcare encounters were obtained from the Children's Hospital of Philadelphia. After applying stringent quality control to remove implausible growth values and including only individuals with all recommended wellness visits by age 7 years, 27,203 (50.78 % male) patients remained for model development. Seven machine learning models were developed to predict obesity incidence as defined by the Centers for Disease Control and Prevention (age/sex adjusted BMI>95th percentile). Model performance was evaluated by multiple standard classifier metrics and the differences among seven models were compared using the Cochran's Q test and post-hoc pairwise testing.
Results:
XGBoost yielded 0.81 (0.001) AUC, which outperformed all other models. It also achieved statistically significant better performance than all other models on standard classifier metrics (sensitivity fixed at 80 %): precision 30.90 % (0.22 %), F1-socre 44.60 % (0.26 %), accuracy 66.14 % (0.41 %), and specificity 63.27 % (0.41 %).
Discussion And Conclusion:
Early childhood obesity prediction models were developed from the largest cohort reported to date. Relative to prior research, our models generalize to include males and females in a single model and extend the time frame for obesity incidence prediction to 7 years of age. The presented machine learning model development workflow can be adapted to various EHR-based studies and may be valuable for developing other clinical prediction models.
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