Comparative Analysis of Life Expectancy between Developed and Developing Countries using Machine Learning

Life Expectancy is an important metric to assess the health of a nation. This paper presents a comparative analysis of life expectancy between developed and developing countries with the help of a Supervised Machine Learning model. The prediction model is trained using three regression models, namely Linear Regression, Decision Tree Regressor and Random Forest Regressor. The selection of model is done on the basis of R
²
score, Mean Squared Error & Mean Absolute Error. Random Forest Regressor is selected for the development of the prediction model for life expectancy, as it had R
²
score as 0.99 and 0.95 on training & testing data respectively, along with 4.43 and 1.58 as the Mean Squared Error & Mean Absolute Error. The comparative analysis is done on the basis of HIV/AIDS, Adult Mortality and Expenditure on Healthcare, as they are the important features suggested by the model. The study undertaken suggests that, developed countries have high life expectancy as compared to developing countries. India has high adult mortality as compared to considered developed countries because of the low expenditure on healthcare. The insights from this analysis can be used by Government and Healthcare sectors for the betterment of society.