⚡ Quick Summary
This study utilized machine learning algorithms to predict abortion among reproductive-age women in Ethiopia, analyzing data from 14,931 women. The random forest classifier emerged as the most effective model, achieving an accuracy of 0.91 and an AUC of 0.97, highlighting the potential of AI in public health research.
🔍 Key Details
- 📊 Dataset: 14,931 women from the 2016 Ethiopian Demographic and Health Survey
- ⚙️ Technology: 7 machine learning algorithms including random forest and XGBoost
- 🏆 Performance: Random forest classifier: Accuracy 0.91, AUC 0.97
- 📈 Metrics used: Accuracy, precision, recall, F1-score, AUC
🔑 Key Takeaways
- 🤖 Machine learning offers improved prediction accuracy for abortion risk compared to traditional methods.
- 🏆 Random forest classifier was the best-performing model with an accuracy of 0.91.
- 📊 XGBoost followed closely with an accuracy of 0.87.
- 👶 Younger age was identified as the strongest predictor of abortion.
- 💑 Having a younger husband was the second most impactful factor.
- 👩👧 First-time childbirth before age 18 ranked third in predictive importance.
- 🌍 Study emphasizes the integration of machine learning in public health initiatives.
- 🔮 Future research should focus on larger datasets and diverse populations.
📚 Background
Abortion remains a significant health concern, leading to complications and maternal deaths, particularly in developing countries like Ethiopia. Traditional statistical methods have been used to identify factors associated with abortion, but they often fall short in capturing the complex relationships within the data. The advent of machine learning presents a promising alternative, allowing for more nuanced predictions and insights into public health issues.
🗒️ Study
This study aimed to leverage machine learning algorithms to predict abortion among women of reproductive age in Ethiopia. By analyzing data from the 2016 Ethiopian Demographic and Health Survey, researchers employed a sample of 14,931 women aged 15-49 years. The study utilized seven different machine learning algorithms, including logistic regression, decision trees, and support vector machines, to classify abortion risk.
📈 Results
The results indicated that the random forest classifier was the most effective model, achieving an impressive accuracy of 0.91 and an AUC of 0.97. The second-best model, XGBoost, also demonstrated strong performance with an accuracy of 0.87 and an AUC of 0.94. The study utilized SHapley Additive Explanations (SHAP) values to identify key predictors, revealing that younger age was the most significant factor influencing abortion risk.
🌍 Impact and Implications
The findings from this study underscore the transformative potential of integrating machine learning into public health research. By accurately predicting abortion risks, healthcare providers can implement targeted interventions and enhance reproductive health services. This approach not only aims to improve maternal health outcomes in Ethiopia but also sets a precedent for similar applications in other regions and contexts.
🔮 Conclusion
This study highlights the remarkable capabilities of machine learning in predicting abortion among reproductive-age women in Ethiopia. The successful application of these algorithms can lead to more informed public health strategies and improved maternal health outcomes. As we look to the future, further research is essential to refine these models and expand their applicability across diverse populations and settings.
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Application of machine learning algorithm for prediction of abortion among reproductive age women in Ethiopia.
Abstract
Abortion is a critical health issue that leads to numerous complications, maternal deaths, and significant financial burdens on women, families, and healthcare systems. Studies have identified factors associated with abortion using traditional statistical analysis methods; however, no previous research has utilized machine learning to predict abortion in Ethiopia or identify its predictive factors. Machine learning is more effective and offers a better solution as it can capture complex and non-linear relationships in the data, leading to improved prediction accuracy compared to traditional regression models. Therefore, this study employed machine learning algorithms to predict abortion in Ethiopia and identify its predictors using nationally representative data. This study used the recent 2016 Ethiopian Demographic and Health Survey and included a sample of 14,931 women of reproductive age (15-49 years). This study used 7 machine learning algorithms for the classification of abortion. The dataset was randomly split into training and testing sets, with 80% allocated for training and 20% for testing. To evaluate the performance of each predictive model, we used a range of metrics such as accuracy, precision, recall, F1-score, and area under the curve (AUC). In this study, SHapley Additive Explanations (SHAP) values were used to measure the influence of each feature on the model’s predictions. In the current study, 7 machine learning algorithm (i.e. logistic regression, decision tree classifier, random forest classifier, support vector machine, K neighbor classifier, XGBoost, and Nave bayes) were applied. The random forest classifier model were the best predictive models with the accuracy of 0.91 and AUC of 0.97. Moreover, the XGBoost was the 2nd best-performing algorithm with 0.87 accuracy and 0.94 AUC. According to the SHAP beeswarm and bar plots, younger age was identified as the strongest predictor of abortion, with a mean SHAP value of + 0.060. The second most impactful factor was having a younger husband, contributing a mean SHAP value of + 0.050 to abortion prediction in Ethiopia. Additionally, giving birth for the first time before the age of 18 ranked third, with a mean SHAP value of + 0.052. This study underscores the value of integrating machine learning into public health research and practice. Future work should focus on refining these models with larger and more diverse datasets, as well as exploring their applicability in other contexts and regions to further global maternal health initiatives. By harnessing machine learning techniques, healthcare providers can better classify abortion risks in reproductive-age women in Ethiopia. This knowledge can inform targeted interventions, enhance reproductive health services, and ultimately improve maternal health outcomes.
Author: [‘Asnake AA’, ‘Gebrehana AK’, ‘Asebe HA’, ‘Seifu BL’, ‘Fente BM’, ‘Bezie MM’, ‘Melkam M’, ‘Tsega SS’, ‘Negussie YM’, ‘Asmare ZA’]
Journal: Sci Rep
Citation: Asnake AA, et al. Application of machine learning algorithm for prediction of abortion among reproductive age women in Ethiopia. Application of machine learning algorithm for prediction of abortion among reproductive age women in Ethiopia. 2025; 15:17924. doi: 10.1038/s41598-025-95342-x