Life Expectancy Prediction through Analysis of Immunization and HDI Factors using Machine Learning Regression Algorithms

Abstract

One of the most crucial elements in end-of-life judgment is life expectancy. For example, good forecasting aids in determining the course of therapy and planning for the acquisition of wellness services and infrastructure. Physicians, on the other hand, tend to overestimate life expectancy, missing the window of opportunity to begin a plan of care. This study examines the feasibility of estimating life expectancy from a WHO dataset collected from Kaggle using machine learning techniques. Even though much research has been conducted in the past on factors influencing life expectancy, including demographic factors, economic distribution, and death rates. It was observed that the impact of immunizations on the standard of living was not previously considered. In this paper, we analyzed life expectancy based on various features, including immunization features (Polio, Hepatitis B, Diphtheria, etc..), HDI factors (schooling, GDP, etc.) of various countries for 15 years period. We also proposed machine learning algorithms for the prediction of life expectancy. We applied regression algorithms logistic regression, SVM, Decision Tree, and random forest regression and achieved a good r-squared value with the random forest algorithm