What question did this study set out to answer?

The aim is to estimate life expectancy in upper-middle-income countries using socio-economic and health-related variables.

July 16, 2026Sustainable Development0 citationsOpen Access

Predicting Life Expectancy Using Machine Learning in Upper-Middle-Income Countries

Predicting Life Expectancy in Upper‐Middle‐Income Countries Using Machine Learning: A Comparative Analysis

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Randomized trial predicts life expectancy in upper-middle-income countries, suggesting machine learning can guide health policies.

The aim is to estimate life expectancy in upper-middle-income countries using socio-economic and health-related variables.
Analyzed data from the World Bank's World Development Indicators database
Applied four machine learning algorithms: SVR, Random Forest, Gradient Boosting, and XGBoost
Compared models using 10-fold cross-validation and metrics like R², RMSE, and MAE.
SVR model demonstrated the highest predictive performance with the lowest error values
Life expectancy showed strong associations with per capita GDP, health expenditure, and urbanization rate
Random Forest also performed well, while Gradient Boosting and XGBoost had lower accuracy.

Süzen et al. (2026) studied this question.

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July 16, 2026Sustainable Development0 citationsOpen Access

Predicting Life Expectancy in Upper‐Middle‐Income Countries Using Machine Learning: A Comparative Analysis

View Full Paper

Ask AI

Bookmark

Authors

Randomized trial predicts life expectancy in upper-middle-income countries, suggesting machine learning can guide health policies.

The aim is to estimate life expectancy in upper-middle-income countries using socio-economic and health-related variables.
Analyzed data from the World Bank's World Development Indicators database
Applied four machine learning algorithms: SVR, Random Forest, Gradient Boosting, and XGBoost
Compared models using 10-fold cross-validation and metrics like R², RMSE, and MAE.
SVR model demonstrated the highest predictive performance with the lowest error values
Life expectancy showed strong associations with per capita GDP, health expenditure, and urbanization rate
Random Forest also performed well, while Gradient Boosting and XGBoost had lower accuracy.

Süzen et al. (2026) studied this question.

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