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Predicting undernutrition among elementary schoolchildren in the Philippines using machine learning algorithms
Vanessa T Siy Van1, Victor A Antonio2, Carmina P Siguin3
1Health Sciences Program, School of Science and Engineering, Ateneo de Manila University, Quezon City, Philippines.
Insights
Machine learning accurately predicts undernutrition in Filipino children using household data. The random forest algorithm showed the best performance, highlighting potential for targeted feeding programs.
Area of Science:
- Nutritional epidemiology
- Machine learning applications in public health
- Dietary assessment
Background:
- Undernutrition remains a significant public health challenge, particularly in developing countries.
- Accurate prediction of undernutrition is crucial for effective intervention strategies.
- Existing methods for classifying nutritional risk may not fully capture the complexities of dietary intake.
Purpose of the Study:
- To compare the accuracy of four machine learning algorithms in predicting undernutrition among Filipino schoolchildren.
- To evaluate two classification schemes, National Academy of Medicine (NAM) acceptable macronutrient distribution ranges (AMDRs) and Philippine Dietary Reference Intakes (PDRIs), for nutritional risk assessment.
- To assess the performance of machine learning predictions against anthropometric classifications used by the national school feeding program.
Main Methods:
- Data from 618 public-school children in the Philippines were collected using 24-h dietary recalls and socioeconomic surveys.
- Nutritional risk was classified using NAM AMDRs and PDRIs.
- Four machine learning algorithms (random forest, support-vector machine, linear discriminant analysis, logistic regression) were employed to predict undernutrition.
- Algorithm performance was evaluated based on accuracy, sensitivity, and specificity.
Main Results:
- The prevalence of undernutrition was higher under NAM AMDRs (82.67%) than PDRIs (78.71%).
- The random forest algorithm demonstrated the highest accuracy (78.55%) in predicting undernutrition.
- Key predictors identified by random forest included household expenditures, child/household age, food insecurity, and dietary diversity.
- AMDR classifications identified more children at risk (477) compared to anthropometric classifications (213).
Conclusions:
- The random forest algorithm is a promising tool for predicting undernutrition in Filipino schoolchildren.
- AMDR classification can aid in targeting beneficiaries for feeding programs.
- Incorporating local dietary culture and addressing data representation for marginalized populations are essential for effective nutrition interventions.
Objectives:
This study aimed to compare the accuracy of four machine-learning (ML) algorithms, using two classification schemes, to predict undernutrition based on individual and household risk factors.
Methods:
Data on public-school children were collected from a rural province (310 children) and a highly urbanized city (308 children) in the Philippines using 24-h dietary recalls and a household socioeconomic and demographic survey. Children's nutritional risk was classified based on acceptable macronutrient distribution ranges (AMDRs) developed by the National Academy of Medicine (NAM) and Philippine Dietary Reference Intakes (PDRIs). Four algorithms (random forest, support-vector machine, linear discriminant analysis, and logistic regression) predicted undernutrition in the sample, and their accuracy, sensitivity, and specificity were compared. Predictions were also compared with the national school feeding program's anthropometric classifications.
Results:
The prevalence of undernutrition was greater under NAM AMDRs (82.67%) compared with PDRI AMDRs (78.71%). Random forest was the most accurate ML algorithm (78.55%), able to predict undernutrition based on household expenditures, child and household age, food insecurity, and dietary diversity. Compared with anthropometric classification (213 children), AMDRs classified more children as at risk for inadequate dietary intake (477 children).
Conclusions:
The random forest algorithm performed best in predicting undernutrition among Filipino elementary schoolchildren, although results could be improved with bootstrap aggregation. The AMDR classification shows potential for targeting feeding beneficiaries. However, local dietary culture should be considered in the development of nutrition interventions. Government use of big-data techniques such as ML must also address underrepresentation in health data collected from and accessible to poor populations or risk further marginalizing them.
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