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Machine learning algorithms for predicting low birth weight in Ethiopia
1Department of Statistics, College of Natural and Computational Sciences, Dire Dawa University, Dire Dawa, Ethiopia. wondesen52@gmail.com.
Insights
Predicting low birth weight (LBW) is crucial for infant survival. The Random Forest model accurately identified LBW in Ethiopia, highlighting key predictors like maternal age and child
Area of Science:
- Public Health
- Medical Informatics
- Machine Learning
Background:
- Low birth weight (LBW) significantly increases infant mortality risk, affecting 14.6% of newborns globally.
- Prevalence of LBW varies regionally, with Africa experiencing higher rates (13.7%) compared to developed regions (7.2%).
- Ethiopia faces a substantial LBW burden, contributing significantly to Africa's statistics, with associated long-term health implications for affected infants.
Purpose of the Study:
- To implement and compare various machine learning models for predicting low birth weight (LBW) in Ethiopia.
- To identify the most effective classifier for accurate LBW prediction using the Ethiopia Demographic and Health Survey 2016 data.
Main Methods:
- Utilized data from the Ethiopia Demographic and Health Survey 2016 for model development.
- Compared eight machine learning classifiers: Logistic Regression, Decision Tree, Naive Bayes, K-Nearest Neighbor, Random Forest (RF), Support Vector Machine, Gradient Boosting, and Extreme Gradient Boosting.
- Performed data preprocessing, including data cleaning, with a binary target category for Normal and LBW infants.
Main Results:
- The Random Forest (RF) model demonstrated superior performance in predicting LBW.
- RF achieved 91.60% accuracy, 91.60% Recall, 96.80% ROC-AUC, and 91.60% F1 Score.
- Key predictors for LBW in Ethiopia included child's gender, birth interval, mother's occupation, and mother's age.
Conclusions:
- The Random Forest model is highly effective for predicting low birth weight in the Ethiopian context.
- Accurate LBW prediction can serve as a vital preventive measure and indicator of infant health risks.
- Identifying critical predictors like maternal and demographic factors can inform targeted interventions to reduce LBW rates.
Background:
Birth weight is a significant determinant of the likelihood of survival of an infant. Babies born at low birth weight are 25 times more likely to die than at normal birth weight. Low birth weight (LBW) affects one out of every seven newborns, accounting for about 14.6 percent of the babies born worldwide. Moreover, the prevalence of LBW varies substantially by region, with 7.2 per cent in the developed regions and 13.7 per cent in Africa, respectively. Ethiopia has a large burden of LBW, around half of Africa. These newborns were more likely to die within the first month of birth or to have long-term implications. These are stunted growth, low IQ, overweight or obesity, developing heart disease, diabetes, and early death. Therefore, the ability to predict the LBW is the better preventive measure and indicator of infant health risks.
Method:
This study implemented predictive LBW models based on the data obtained from the Ethiopia Demographic and Health Survey 2016. This study was employed to compare and identify the best-suited classifier for predictive classification among Logistic Regression, Decision Tree, Naive Bayes, K-Nearest Neighbor, Random Forest (RF), Support Vector Machine, Gradient Boosting, and Extreme Gradient Boosting.
Results:
Data preprocessing is conducted, including data cleaning. The Normal and LBW are the binary target category in this study. The study reveals that RF was the best classifier and predicts LBW with 91.60 percent accuracy, 91.60 percent Recall, 96.80 percent ROC-AUC, 91.60 percent F1 Score, 1.05 percent Hamming loss, and 81.86 percent Jaccard score.
Conclusion:
The RF predicted the occurrence of LBW more accurately and effectively than other classifiers in Ethiopia Demographic Health Survey. Gender of the child, marriage to birth interval, mother's occupation and mother's age were Ethiopia's top four critical predictors of low birth weight in Ethiopia.
