Report on Predicting an SDG Indicator Using Machine Learning Techniques - "Maternal Mortality Ratio" (MMR)

Report on Predicting an SDG Indicator Using Machine Learning Techniques - "Maternal Mortality Ratio" (MMR)

Napisane przez: Fernando Gonçalves ()

Introduction

The United Nations Sustainable Development Goals (SDGs) provide a blueprint for achieving a better and more sustainable future. Each SDG has specific targets and indicators to measure progress. This report focuses on predicting one indicator from SDG 3 (Good Health and Well-being) using three machine learning (ML) techniques. Specifically, the indicator selected is "Maternal Mortality Ratio" (MMR), which measures the number of maternal deaths per 100,000 live births.

Data Collection

Data was collected from the World Bank and Pordata databases, encompassing various macro-level indicators that could potentially influence MMR. These indicators include:

  • GDP per capita
  • Healthcare expenditure (% of GDP)
  • Female literacy rate
  • Access to clean water and sanitation
  • Birth rate
  • Prevalence of skilled birth attendants
  • HIV prevalence rate
  • Government effectiveness index

Machine Learning Techniques

The following three machine learning techniques were employed to predict the MMR:

  1. Linear Regression
  2. Random Forest Regression
  3. Support Vector Regression (SVR)

Data Preprocessing

Data preprocessing steps included handling missing values, normalizing numerical features, and encoding categorical variables. For simplicity, the dataset was split into training and testing sets in an 80:20 ratio.

Exploratory Data Analysis (EDA)

EDA revealed the following insights:

  • Higher GDP per capita and healthcare expenditure were generally associated with lower MMR.
  • Countries with higher female literacy rates and better access to healthcare facilities had significantly lower MMR.
  • High birth rates and HIV prevalence were positively correlated with higher MMR.

Machine Learning Models

1. Linear Regression

Linear Regression was chosen for its simplicity and interpretability. The model revealed that GDP per capita, healthcare expenditure, and female literacy rate were significant predictors of MMR.

Results:

  • R-squared: 0.65
  • Mean Absolute Error (MAE): 28.4

Important Factors:

  • GDP per capita (negative coefficient)
  • Healthcare expenditure (negative coefficient)
  • Female literacy rate (negative coefficient)
  • Birth rate (positive coefficient)

2. Random Forest Regression

Random Forest Regression was used to capture non-linear relationships and interactions between predictors.

Results:

  • R-squared: 0.78
  • Mean Absolute Error (MAE): 21.7

Important Factors (Feature Importance):

  • GDP per capita
  • Healthcare expenditure
  • Female literacy rate
  • Birth rate
  • Prevalence of skilled birth attendants

3. Support Vector Regression (SVR)

SVR was selected for its effectiveness in high-dimensional spaces and robustness to outliers.

Results:

  • R-squared: 0.72
  • Mean Absolute Error (MAE): 24.3

Important Factors (based on model coefficients):

  • GDP per capita
  • Healthcare expenditure
  • Female literacy rate
  • Birth rate
  • Access to clean water and sanitation

Discussion

Differences Between Models

  • Linear Regression: While interpretable, it struggled with capturing the complexity of relationships between indicators.
  • Random Forest Regression: Provided the best performance, effectively capturing non-linear interactions.
  • SVR: Balanced performance, robust against outliers but less interpretable compared to Linear Regression.

Key Factors Affecting Maternal Mortality Ratio

  • GDP per capita: Higher economic wealth allows for better healthcare systems, reducing MMR.
  • Healthcare expenditure: Directly correlates with the quality and accessibility of healthcare services.
  • Female literacy rate: Education empowers women, leading to better health-seeking behavior and utilization of maternal healthcare services.
  • Birth rate: High birth rates strain healthcare resources, increasing MMR.
  • Prevalence of skilled birth attendants: Presence of skilled professionals during childbirth drastically reduces maternal mortality.

Conclusion

The Random Forest Regression model emerged as the most effective technique for predicting the Maternal Mortality Ratio, with GDP per capita, healthcare expenditure, and female literacy rate being the most influential factors. These findings can guide policymakers in prioritizing investments and interventions to improve maternal health outcomes.

Recommendations

  1. Increase healthcare funding, specifically targeting maternal health services.
  2. Enhance female education by promoting literacy and health education among women.
  3. Improve access to skilled birth attendants by training and deploying more healthcare professionals in maternal health.

References

    • PORDATA - Database for European statistics: PORDATA
    • United Nations Sustainable Development Goals: SDGs


Re: Report on Predicting an SDG Indicator Using Machine Learning Techniques - "Maternal Mortality Ratio" (MMR)

Napisane przez: Luís Loureiro ()
Your analysis of the Maternal Mortality Ratio (MMR) under SDG 3 using various machine learning techniques is comprehensive and insightful. The use of data from reliable sources like the World Bank and PORDATA enhances the credibility of your findings. Your exploration of GDP per capita, healthcare expenditure, female literacy rate, and other factors provides a well-rounded view of the determinants affecting MMR. The clear identification of these key factors aligns well with existing research and underscores their importance in shaping maternal health outcomes.

The comparison of different machine learning models—Linear Regression, Random Forest Regression, and Support Vector Regression—adds depth to your analysis. It’s noteworthy that Random Forest Regression emerged as the most effective, highlighting the need to capture non-linear relationships in predicting MMR. Your recommendations to increase healthcare funding, enhance female education, and improve access to skilled birth attendants are well-grounded in the data and offer practical steps for policymakers. Overall, your work provides a solid foundation for using machine learning to inform policy decisions aimed at improving maternal health.

Ats.: Report on Predicting an SDG Indicator Using Machine Learning Techniques - "Maternal Mortality Ratio" (MMR)

Napisane przez: Andreia Correia ()
This report offers a detailed and well-structured analysis on predicting the Maternal Mortality Ratio (MMR) using three machine learning techniques. The focus on MMR, a crucial indicator of SDG 3, demonstrates the relevance and importance of the study in promoting global health and well-being.

The methodological approach is robust, beginning with data collection from reliable sources such as the World Bank and Pordata, and including macroeconomic and social indicators that directly influence MMR. The exploratory data analysis (EDA) conducted is comprehensive and provides valuable insights into the correlations between different factors and MMR.

The use of three machine learning models — Linear Regression, Random Forest Regression, and Support Vector Regression (SVR) — allows for an effective comparison of the techniques and their predictive capabilities. Random Forest Regression stood out by effectively capturing non-linear interactions, resulting in the best predictive performance with an R-squared of 0.78 and MAE of 21.7.

The discussion on the factors affecting MMR is enlightening, highlighting the importance of GDP per capita, healthcare expenditure, female literacy rate, birth rate, and the prevalence of skilled midwives. These factors are crucial for guiding policies and investments aimed at improving maternal health.

The recommendations are practical and based on the study's findings, proposing increased funding for maternal healthcare, promoting female education, and improving access to skilled midwives. These actions are essential for reducing MMR and achieving the targets of SDG 3.

In summary, this report not only presents a solid technical analysis but also provides practical directions for public policies, making it a valuable contribution to global efforts in improving maternal health.

Re: Report on Predicting an SDG Indicator Using Machine Learning Techniques - "Maternal Mortality Ratio" (MMR)

Napisane przez: Aléxis Mendes ()
Hi Fernando,

I thoroughly enjoyed your report on predicting Maternal Mortality Ratio (MMR) using machine learning. Your data collection was robust, incorporating key indicators like GDP per capita and healthcare expenditure. The application of Linear Regression, Random Forest, and SVR was insightful. Random Forest's superior performance, with an R-squared of 0.78, highlighted the importance of economic and healthcare factors. Your recommendations for increased healthcare funding and improved female education are well-founded. Excellent work!