An Intelligent XGBoost-Based Machine Learning Framework for Accurate Life Expectancy Prediction Across Developed and Developing Countries
Authors-S.Gouthami, Yeguri Supriya
Keyword-Life Expectancy Prediction, XGBoost, Machine Learning, Feature Selection, M5P Algorithm, RandomizedSearchCV, Healthcare Analytics, Predictive Modeling, Regression Analysis, Public Health.
Life expectancy is one of the most important indicators used to evaluate the overall health status, healthcare quality, and socio-economic development of a country. Accurate prediction of life expectancy enables governments and healthcare organizations to formulate effective public health policies and allocate medical resources more efficiently. In recent years, machine learning tech-niques have demonstrated superior capability in modeling complex healthcare datasets compared with conventional statistical approaches. This study presents a robust life expectancy prediction framework based on the Extreme Gradient Boosting (XGBoost) algorithm for estimating life expectancy in both developed and developing countries. Initially, the collected dataset undergoes comprehensive preprocessing, including missing value imputation, normalization, and categori-cal encoding to improve data quality. The M5P decision tree algorithm is then employed to iden-tify the most informative attributes influencing life expectancy. Subsequently, the processed data are divided into training and testing subsets, and the XGBoost regression model is optimized using RandomizedSearchCV to determine the most suitable hyperparameter configuration. Mod-el performance is evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). Experimental analysis indicates that the optimized XGBoost model achieves excellent predictive performance with a testing R² score of 0.97, MAE of 1.03, and MSE of 2.45, outperforming several conven-tional machine learning approaches. Feature importance analysis further reveals that HIV/AIDS prevalence, adult mortality, and income composition are among the strongest determinants affect-ing life expectancy. The proposed framework provides an efficient and reliable decision-support tool for healthcare planners, policymakers, and researchers working toward improving global health outcomes.
Doi-[https://doi.org/10.5281/zenodo.21703353]