Machine learning algorithms for predicting life expectancy

Abstract

In the last years SARS-Covid has had a big impact on mortality rate and also on life expectancy. Today the artificial intelligence techniques and machine learning algorithms can analyze large amounts of data and can be used to predict life expectancy. Life expectancy (LE) models have an immense effect on the social and financial structures of many countries around the world.  This paper is an attempt to find the most efficient algorithm and methodology to predict it. Life expectancy depends on various variables like mortality rate, life expectancy from the past years, alcohol consumption rate, infant death, covid mortality and other illness that affect life expectancy rate. In this analysis it is reviewed different machine learning algorithms that have achieved better accuracy based on pertinent features of the datasets. Based on the analyze of simple linear regression and k-nearest neighbors (KNN) algorithms, machine learning techniques were applied in order to develop an accurate prediction solution for life expectancy with the effect COVID-19. By comparing these machine learning algorithms, it is analyzed which among them is more accurate to predict life expectancy.