Prediction of Human Development Index with Health Indicators Using Tree-Based Regression Models

Abstract

Machine learning is a field of artificial intelligence that allows computers to predict and model future events by making inferences from past information with mathematical and statistical operations. In this study, we used tree-based regression models, one of the machine learning methods, to determine and predict the effect of health indicators of 191 countries on the human development index (HDI) between 2014 and 2018 years. When tree-based regression models were compared according to model performance criteria, it was found that the best model was the gradient boosting model with the highest R2 = 0.9962 and the smallest RMSE = 0.0094. With the gradient boosting model, the three most important variables to HDI are; current health expenditure per capita, physicians and nurses, and midwives, respectively. By selecting the ten countries with the highest HDI values and Turkey, HDI values were estimated for 2018-2019 with a gradient boosting model. The countries for which HDI values are best predicted by the gradient boosting method are Netherlands, Sweden, Norway, Iceland, Denmark, Turkey, Ireland, Germany, Australia, and China.