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Predicting and identifying factors associated with undernutrition among children under five years in Ghana using
Eric Komla Anku1, Henry Ofori Duah2
1Dietherapy and Nutrition, Cape Coast Teaching Hospital, Cape Coast, Ghana.
Insights
Machine learning accurately predicts child undernutrition in Ghana. The XGBoost model excelled in identifying key factors like age and sex for targeted interventions to combat stunting, wasting, and underweight in children under five.
Area of Science:
- Public Health
- Machine Learning
- Pediatrics
Background:
- Childhood undernutrition is a critical global health issue, particularly in developing nations.
- Machine learning (ML) offers potential for predicting undernutrition and its determinants.
Purpose of the Study:
- To employ ML algorithms for predicting undernutrition (stunting, wasting, underweight) in children under five.
- To identify significant predictors associated with childhood undernutrition.
Main Methods:
- Secondary data analysis of the 2017 Multiple Indicator Cluster Survey (MICS) using R and Python.
- Trained and evaluated seven ML algorithms: LDA, logistic regression, SVM, RF, LASSO, Ridge, and XGBoost.
- Assessed model performance using accuracy, confusion matrix, and ROC AUC.
Main Results:
- XGBoost demonstrated superior performance with 98% accuracy for wasting, stunting, and underweight.
- XGBoost achieved 100% AUC for wasting and stunting, indicating high predictive power.
- Key predictors identified include age, weight, height, sex, region, and ethnicity.
Conclusions:
- The XGBoost model is highly effective for predicting childhood undernutrition in Ghana.
- ML algorithms can effectively identify crucial predictors for developing targeted interventions.
- Findings support the use of ML for public health strategies addressing child undernutrition.
Background:
Undernutrition among children under the age of five is a major public health concern, especially in developing countries. This study aimed to use machine learning (ML) algorithms to predict undernutrition and identify its associated factors.
Methods:
Secondary data analysis of the 2017 Multiple Indicator Cluster Survey (MICS) was performed using R and Python. The main outcomes of interest were undernutrition (stunting: height-for-age (HAZ) < -2 SD; wasting: weight-for-height (WHZ) < -2 SD; and underweight: weight-for-age (WAZ) < -2 SD). Seven ML algorithms were trained and tested: linear discriminant analysis (LDA), logistic model, support vector machine (SVM), random forest (RF), least absolute shrinkage and selection operator (LASSO), ridge regression, and extreme gradient boosting (XGBoost). The ML models were evaluated using the accuracy, confusion matrix, and area under the curve (AUC) receiver operating characteristics (ROC).
Results:
In total, 8564 children were included in the final analysis. The average age of the children was 926 days, and the majority were females. The weighted prevalence rates of stunting, wasting, and underweight were 17%, 7%, and 12%, respectively. The accuracies of all the ML models for wasting were (LDA: 84%; Logistic: 95%; SVM: 92%; RF: 94%; LASSO: 96%; Ridge: 84%, XGBoost: 98%), stunting (LDA: 86%; Logistic: 86%; SVM: 98%; RF: 88%; LASSO: 86%; Ridge: 86%, XGBoost: 98%), and for underweight were (LDA: 90%; Logistic: 92%; SVM: 98%; RF: 89%; LASSO: 92%; Ridge: 88%, XGBoost: 98%). The AUC values of the wasting models were (LDA: 99%; Logistic: 100%; SVM: 72%; RF: 94%; LASSO: 99%; Ridge: 59%, XGBoost: 100%), for stunting were (LDA: 89%; Logistic: 90%; SVM: 100%; RF: 92%; LASSO: 90%; Ridge: 89%, XGBoost: 100%), and for underweight were (LDA: 95%; Logistic: 96%; SVM: 100%; RF: 94%; LASSO: 96%; Ridge: 82%, XGBoost: 82%). Age, weight, length/height, sex, region of residence and ethnicity were important predictors of wasting, stunting and underweight.
Conclusion:
The XGBoost model was the best model for predicting wasting, stunting, and underweight. The findings showed that different ML algorithms could be useful for predicting undernutrition and identifying important predictors for targeted interventions among children under five years in Ghana.
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