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A Point-of-Care Method with Integrated Decision Support Tool to Estimate Anemia at Population Level
Published on: January 19, 2024
An ensemble machine learning approach for predicting anemia among under-five children in malaria-endemic sub-Saharan
Berhan Tekeba1, Nebebe Demis Baykemagn2, Alexander Takele Mengesha3
1Department of Pediatrics and Child Health Nursing, School of Nursing, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia. berishboss7@gmail.com.
Insights
Anemia in children under five is a major health issue in sub-Saharan Africa. Machine learning identified region, birth order, child age, wealth, and mosquito net use as key predictors for targeted interventions.
Area of Science:
- Public Health
- Machine Learning
- Pediatrics
Background:
- Anemia affects children under five globally, with a high prevalence in malaria-endemic sub-Saharan Africa.
- Malaria co-exists with high anemia rates in sub-Saharan Africa, necessitating integrated health strategies.
Purpose of the Study:
- To develop an ensemble machine learning model for estimating anemia burden.
- To identify key predictors of anemia in children under five in malaria-endemic regions of sub-Saharan Africa.
Main Methods:
- A cross-sectional study utilized Demographic and Health Survey data from sub-Saharan African countries.
- An ensemble machine learning model was developed, employing SMOTE and Tomek Links for data balancing.
- Recursive Feature Elimination with Random Forest identified anemia predictors.
Main Results:
- The XGBoost model achieved high performance: 83.69% accuracy, 85.81% precision, 83.19% F1 score, 90.1 ROC AUC, and 90.0 Precision Recall AUC.
- Key predictors identified include region, birth order, child age, wealth index, and mosquito net ownership.
Conclusions:
- Interventions should be geographically targeted and focus on younger children and those with high birth orders.
- Integrating anemia screening into routine check-ups is recommended.
- Enhancing economic support and promoting mosquito net use are crucial for reducing anemia.
Background:
Worldwide, anemia in children under-five is a major public health issue, particularly in sub-Saharan Africa. Sub-Saharan Africa also has the highest burden of malaria. This study aimed to develop an ensemble machine learning model to estimate anemia burden and potential predictors in under-five children in malaria-endemic sub-Saharan African countries.
Method:
A cross-sectional study was conducted using Demographic and Health Survey data from sub-Saharan African countries. Samples were selected through a two-stage stratified cluster sampling method. Data analysis was performed using Python 3.8, with a total weighted sample of 21,249. The dataset was split into 80% for training and 20% for testing and validation purposes. To address class imbalance, a hybrid data balancing approach combining SMOTE (Synthetic Minority Over-sampling Technique) and Tomek Links was applied. Four machine learning algorithms were developed and evaluated using standard performance metrics. Recursive Feature Elimination with a Random Forest classifier was used to identify potential predictors of anemia among children under five living in malaria-endemic SSA countries.
Result:
In this study, XGBoost showed the best performance, achieving an accuracy of 83.69%, a precision of 85.81%, and an F1 score of 83.19%. Additionally, XGBoost attained the highest ROC AUC of 90.1 and Precision Recall AUC of 90.0. According to Recursive Feature Elimination with a Random Forest classifier, region, birth order, child age, wealth index, and number of mosquito nets were identified as the associated factors of anemia among under-five children in malaria-endemic SSA countries.
Conclusion:
To reduce anemia among under-five children in malaria-endemic regions of sub-Saharan Africa, interventions should prioritize implementing geographically targeted programs, focus on younger children and those with high birth orders by integrating anemia screening into routine check-ups. In addition, enhancing economic support for low-income families and distributing and educating families on the proper use of mosquito nets are essential.
