Statistical Analysis on Prediction and Analysis of Life Expectancy

Abstract

   Life Expectancy (LE) models have vast effects on many countries' social and financial structures around the world. Many studies have suggested the essential implications of Life expectancy predictions on social aspects and healthcare system management around the globe. These models provide many ways to improve healthcare and advanced care planning mechanism related to society. However, with time, it was observed that many present determinants were not enough to predict the generic set of populations' longevity. Previous models were based upon mortality-based knowledge of the targeted sampling population. With the advancement in forecasting technologies and rigorous work of past individuals has proposed that other than mortality rate, there are still many factors needed to be addressed to deduce the standard Predicated Life expectancy models (PrLE). Due to this, now Life expectancy is being studied with some additional set of interests into educational, health, economic, and social welfare services. Our objective here will be to briefly discuss the previously used machine learning, Ensemble methods and hybrid methods used in building life expectancy prediction models. We will aim to test the potential and accuracy of existing ML techniques and their probabilistic projections to predict the generic Life expectancy of a particular region, state.