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Predicting Childhood Obesity Based on Single and Multiple Well-Child Visit Data Using Machine Learning Classifiers
Pritom Kumar Mondal1, Kamrul H Foysal2, Bryan A Norman1
1Department of Industrial, Manufacturing & Systems Engineering, Texas Tech University, Lubbock, TX 79409, USA.
Insights
Predicting childhood obesity early is crucial for prevention. New machine learning models accurately forecast a child's obesity risk using basic health data, aiding healthcare professionals in early intervention.
Area of Science:
- Pediatrics
- Public Health
- Machine Learning
Background:
- Childhood obesity is a significant public health issue in the U.S., leading to severe comorbidities.
- Current methods for obesity assessment lack predictive capabilities for future risk.
- Existing predictive models often require extensive longitudinal data and numerous variables.
Purpose of the Study:
- To develop and evaluate novel machine learning techniques for early childhood obesity prediction.
- To address limitations of current methods by utilizing readily available patient data.
- To provide a decision support tool for healthcare professionals to identify at-risk children.
Main Methods:
- Proposed three distinct machine learning models for different data availability scenarios.
- Utilized datasets including birth BMI, gestational age, well-child visit BMI measures, and gender.
- Applied models to predict obesity status at five years of age.
Main Results:
- Achieved prediction accuracies of 89%, 77%, and 89% for the three distinct scenarios.
- Demonstrated effective prediction even with limited or non-longitudinal data.
- Models successfully categorized children into normal weight, overweight, or obese categories.
Conclusions:
- The developed machine learning models offer a viable approach for early childhood obesity risk prediction.
- These models can function as decision support tools, enabling timely interventions.
- Early prediction and intervention can mitigate long-term health complications associated with childhood obesity.
Abstract:
Childhood obesity is a public health concern in the United States. Consequences of childhood obesity include metabolic disease and heart, lung, kidney, and other health-related comorbidities. Therefore, the early determination of obesity risk is needed and predicting the trend of a child's body mass index (BMI) at an early age is crucial. Early identification of obesity can lead to early prevention. Multiple methods have been tested and evaluated to assess obesity trends in children. Available growth charts help determine a child's current obesity level but do not predict future obesity risk. The present methods of predicting obesity include regression analysis and machine learning-based classifications and risk factor (threshold)-based categorizations based on specific criteria. All the present techniques, especially current machine learning-based methods, require longitudinal data and information on a large number of variables related to a child's growth (e.g., socioeconomic, family-related factors) in order to predict future obesity-risk. In this paper, we propose three different techniques for three different scenarios to predict childhood obesity based on machine learning approaches and apply them to real data. Our proposed methods predict obesity for children at five years of age using the following three data sets: (1) a single well-child visit, (2) multiple well-child visits under the age of two, and (3) multiple random well-child visits under the age of five. Our models are especially important for situations where only the current patient information is available rather than having multiple data points from regular spaced well-child visits. Our models predict obesity using basic information such as birth BMI, gestational age, BMI measures from well-child visits, and gender. Our models can predict a child's obesity category (normal, overweight, or obese) at five years of age with an accuracy of 89%, 77%, and 89%, for the three application scenarios, respectively. Therefore, our proposed models can assist healthcare professionals by acting as a decision support tool to aid in predicting childhood obesity early in order to reduce obesity-related complications, and in turn, improve healthcare.
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