Global Life Expectancy Prediction Using Machine Learning Ensemble Techniques

Global life expectancy (GLE) is the typical lifespan that an individual is expected to live on, determined statistically from data. In this study, it is predicted using a dataset of social, economic, health, and demographic variables from different nations to make robust predictive algorithms. This study focuses on using machine learning (ML) ensemble models to predict GLE. This involves using key factors from the dataset that influence GLE. In this study, we utilized ML ensemble models like Random Forest (RF), Light Gradient Boosting Machine (LGBM), Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGB) for the prediction of GLE. The result showed that LGBM outperformed other models by scoring 1.1851, 1.8778, 0.9504, 0.9504, and 3.5259 as MAE, RMSE, R2, EVS and MSE respectively. The result can be used in healthcare policies, planning, budgeting, and allocations to enhance GLE. Also, we employed explainable artificial intelligence (XAI) for a universal interpretation of the model’s intricate performances. This study has the potential to contribute to the development of evidence-based policies and interventions to enhance global public health using precise GLE made through the application of ML ensemble techniques.