Machine Learning-Based Life Expectancy Prediction in Developed and Developing Regions

The life expectancy of a population is a vital measure of its overall health and healthcare quality. This study use machine learning methods, notably XGBoost, to predict life expectancy in industrialized and emerging countries. XGBoost, or Extreme Gradient Boosting, is a robust machine-learning technique renowned for its precision and efficiency in handling structured data. The objective is to develop a precise prediction model and identify the primary factors influencing life expectancy. The experimental findings indicate that our XGBoost-based model effectively forecasts life expectancy in both industrialized and developing nations. Mean Absolute Error, Mean Squared Error, and R² score are used for model selection. XGBoost is selected because to its mean squared error of 2.45, mean absolute error of 1.03, and R² scores of 0.99 for the training set and 0.97 for the testing set. We conduct feature significance analysis to identify the most relevant factors affecting life expectancy. Our results underscore the significance of variables such as HIV/AIDS, income composition, and adult mortality in influencing life expectancy. The model's findings may assist lawmakers and healthcare professionals in making educated decisions to enhance public health outcomes, allocate resources efficiently, and formulate focused solutions.