⚡ Quick Summary
A recent study utilized machine learning techniques to analyze and forecast under-5 mortality trends in Bangladesh, revealing a significant decline of 76.72% from 1994 to 2018. The Linear Regression model proved to be the most accurate, predicting future rates of 29.87 deaths per 1,000 live births by 2030.
🔍 Key Details
- 📊 Dataset: Bangladesh Demographic and Health Survey (BDHS) from 1993-94 to 2017-18
- 🧩 Features used: Various socio-economic and health indicators
- ⚙️ Technology: Machine learning models including Linear Regression, Ridge Regression, and XGBoost
- 🏆 Performance: Linear Regression achieved MAE of 4.05, RMSE of 4.56, and R-squared of 0.98
🔑 Key Takeaways
- 📉 Significant decline in under-5 mortality in Bangladesh over the past two decades.
- 🤖 Machine learning models provided accurate forecasts for future mortality trends.
- 🏆 Linear Regression was the most effective model, outperforming others in accuracy metrics.
- 🔮 Future projections indicate a need for intensified healthcare interventions to meet SDG targets.
- 🌍 The study highlights the importance of data-driven approaches in public health policy.
- 📅 Long-term predictions should be approached with caution due to socio-economic uncertainties.
- 💡 Insights from this study can guide policymakers in improving healthcare access and maternal health.
📚 Background
Under-5 mortality is a crucial indicator of a country’s health and development, particularly in developing nations like Bangladesh. The persistent challenges in reducing these rates necessitate innovative approaches, such as machine learning, to analyze trends and forecast future outcomes effectively.
🗒️ Study
The study analyzed data from the Bangladesh Demographic and Health Survey (BDHS) spanning from 1993-94 to 2017-18. By employing various machine learning algorithms, including Linear Regression, Ridge Regression, and others, the researchers aimed to provide actionable insights for health professionals and policymakers.
📈 Results
The findings confirmed a remarkable decline in under-5 mortality rates, with the Linear Regression model achieving the lowest Mean Absolute Error (MAE) of 4.05 and a high R-squared value of 0.98. Projections suggest that under-5 mortality could decrease to 29.87 per 1,000 live births by 2030 and 26.21 by 2035.
🌍 Impact and Implications
The implications of this study are profound, as it underscores the potential of machine learning in public health. By providing accurate forecasts, policymakers can better allocate resources and implement targeted interventions to improve healthcare access and maternal health, ultimately striving to meet the Sustainable Development Goal (SDG) of 25 deaths per 1,000 live births by 2030.
🔮 Conclusion
This study highlights the transformative power of machine learning in analyzing public health data and forecasting trends. While the results are promising, they also emphasize the need for continued efforts and interventions to achieve global health targets. The future of healthcare analytics looks bright, and further research in this area is essential for sustained progress.
💬 Your comments
What are your thoughts on the use of machine learning in public health forecasting? We would love to hear your insights! 💬 Join the conversation in the comments below or connect with us on social media:
Analyzing and forecasting under-5 mortality trends in Bangladesh using machine learning techniques.
Abstract
BACKGROUND: Under-5 mortality remains a critical social indicator of a country’s development and economic sustainability, particularly in developing nations like Bangladesh. This study employs machine learning models, including Linear Regression, Ridge Regression, Lasso Regression, Bayesian Ridge, Decision Tree, Gradient Boosting, XGBoost, and CatBoost, to forecast future trends in under-5 mortality. By leveraging these models, the study aims to provide actionable insights for policymakers and health professionals to address persistent challenges.
METHODS: Data from the 1993-94 to 2017-18 Bangladesh Demographic and Health Survey (BDHS) was analyzed using advanced machine learning algorithms. Key metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R-squared, and Mean Absolute Percentage Error (MAPE), were employed to evaluate model performance. Additionally, k-fold cross-validation was conducted to ensure robust model evaluation.
RESULTS: This study confirms a significant decline in under-5 mortality in Bangladesh over the study period, with machine learning models providing accurate predictions of future trends. Among the models, Linear Regression emerged as the most accurate, achieving the lowest MAE (4.05), RMSE (4.56), and MAPE (6.64%), along with the highest R-squared value (0.98). Projections indicate further reductions in under-5 mortality to 29.87 per 1,000 live births by 2030 and 26.21 by 2035.
CONCLUSIONS: From 1994 to 2018, under-5 mortality in Bangladesh decreased by 76.72%. While the Linear Regression model demonstrated exceptional accuracy in forecasting trends, long-term predictions should be interpreted cautiously due to inherent uncertainties in socio-economic conditions. The forecasted rates fall short of the Sustainable Development Goal (SDG) target of 25 deaths per 1,000 live births by 2030, underscoring the need for intensified interventions in healthcare access and maternal health to achieve this target.
Author: [‘Naznin S’, ‘Uddin MJ’, ‘Ahmad I’, ‘Kabir A’]
Journal: PLoS One
Citation: Naznin S, et al. Analyzing and forecasting under-5 mortality trends in Bangladesh using machine learning techniques. Analyzing and forecasting under-5 mortality trends in Bangladesh using machine learning techniques. 2025; 20:e0317715. doi: 10.1371/journal.pone.0317715