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Comparative analysis of machine learning algorithms for predicting diarrhea among under-five children in Ethiopia:
Alemu Birara Zemariam1, Wondosen Abey2, Abdulaziz Kebede Kassaw3
1Department of Pediatrics and Child Health Nursing, School of Nursing, College of Medicine and Health Science, Woldia University, Woldia, Ethiopia.
Insights
The Random Forest model effectively predicted childhood diarrhea in Ethiopia, achieving 93.2% accuracy. Key predictors included residence, wealth, and child age, guiding targeted public health interventions.
Area of Science:
- Public Health
- Biostatistics
- Machine Learning
Background:
- Diarrhea remains a leading cause of mortality and morbidity in children under five globally, particularly in developing nations like Ethiopia.
- Existing research on predicting childhood diarrhea using machine learning (ML) is limited.
- Ethiopia faces a significant burden of childhood diarrhea, necessitating improved predictive strategies.
Purpose of the Study:
- To compare the predictive performance of various machine learning algorithms for childhood diarrhea in Ethiopia.
- To identify key predictors associated with diarrhea incidence in under-five children.
- To explore the utility of association rule mining for understanding diarrhea-related factors.
Main Methods:
- Utilized a dataset of 9501 under-five children from the Ethiopia Demographic and Health Survey 2016.
- Employed five machine learning algorithms (including Random Forest) for predictive modeling, with performance evaluated using metrics in Python.
- Applied Boruta feature selection, data balancing techniques (e.g., SMOTE), hyperparameter tuning, and association rule mining (Apriori algorithm in R).
Main Results:
- 10.2% of children experienced diarrhea.
- The Random Forest model demonstrated superior performance: 93.2% accuracy, 98.4% sensitivity, 85.5% specificity, and 0.916 AUC.
- Top predictors identified were residence, wealth index, child age, number of living children, deworming status, wasting, mother's occupation, and education.
Conclusions:
- The Random Forest model is highly effective for predicting childhood diarrhea in the Ethiopian context.
- Findings provide actionable insights for policymakers and healthcare providers to develop targeted interventions.
- Customized strategies based on identified association rules can significantly improve child health outcomes and reduce the impact of diarrhea.
Abstract:
Background: Diarrhea is a major cause of mortality and morbidity in under-5 children globally, especially in developing countries like Ethiopia. Limited research has used machine learning to predict childhood diarrhea. This study aimed to compare the predictive performance of ML algorithms for diarrhea in under-5 children in Ethiopia. Methods: The study utilized a dataset of 9501 under-5 children from the Ethiopia Demographic and Health Survey 2016. Five ML algorithms were used to build and compare predictive models. The model performance was evaluated using various metrics in Python. Boruta feature selection was employed, and data balancing techniques such as under-sampling, over-sampling, adaptive synthetic sampling, and synthetic minority oversampling as well as hyper parameter tuning methods were explored. Association rule mining was conducted using the Apriori algorithm in R to determine relationships between independent and target variables. Results: 10.2% of children had diarrhea. The Random Forest model had the best performance with 93.2% accuracy, 98.4% sensitivity, 85.5% specificity, and 0.916 AUC. The top predictors were residence, wealth index, and child age, number of living children, deworming, wasting, mother's occupation, and education. Association rule mining identified the top 7 rules most associated with under-5 diarrhea in Ethiopia. Conclusion: The RF achieved the highest performance for predicting childhood diarrhea. Policymakers and healthcare providers can use these findings to develop targeted interventions to reduce diarrhea. Customizing strategies based on the identified association rules has the potential to improve child health and decrease the impact of diarrhea in Ethiopia.
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