HUMAN LIFE EXPECTANCY PREDICTION USING MACHINE LEARNING

Many countries' societal and economic structures are profoundly impacted by life expectancy (LE) models. Life expectancy forecasts have been shown to have far-reaching effects on healthcare system administration and social policy in several research. The healthcare system and the mechanism for advanced care planning may both benefit greatly from the insights provided by these models. Many current determinants were once thought to be sufficient for predicting the longevity of the generic set of population, but it has become clear that this is not the case. Previous models relied on data gleaned from the population's death rate. Despite improvements in forecasting methods and years of hard work, some researchers have pointed out that many factors beyond mortality rate need to be taken into account before Predicted Life Expectancy (PrLE) models can be derived. As a result, researchers are examining life expectancy alongside a wider range of topics, including healthcare, economics, and social support. The authors of the Analysis use a variety of machine learning methods to improve accuracy depending on specific characteristics of the dataset. Many countries' societal and economic structures are profoundly impacted by life expectancy (LE) models. Life expectancy forecasts have been shown to have far-reaching effects on healthcare system administration and social policy in several research. The healthcare system and the mechanism for advanced care planning may both benefit greatly from the insights provided by these models. Many current determinants were once thought to be sufficient for predicting the longevity of the generic set of population, but it has become clear that this is not the case. Previous models relied on data gleaned from the population's death rate. Despite improvements in forecasting methods and years of hard work, some researchers have pointed out that many factors beyond mortality rate need to be taken into account before Predicted Life Expectancy (PrLE) models can be derived. As a result, researchers are examining life expectancy alongside a wider range of topics, including healthcare, economics, and social support. The authors of the Analysis use a variety of machine learning methods to improve accuracy depending on specific characteristics of the dataset.Life expectancy (LE) models have vast effects on the social and financial structures of many countries 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 longevity of the generic set of population. Previous models were based upon mortality-based knowledge of the targeted sampling population. With the advancement in forecasting technologies and rigorous work of the past, individuals have proposed this fact that other than mortality rate, there are still many factors needed to be addressed in order to deduce the standard Predicted 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. In the Analysis, the authors have implemented different machine learning algorithms and have achieved better accuracy based on pertinent features of the dataset Life expectancy (LE) models have vast effects on the social and financial structures of many countries 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 longevity of the generic set of population. Previous models were based upon mortality-based knowledge of the targeted sampling population. With the advancement in forecasting technologies and rigorous work of the past, individuals have proposed this fact that other than mortality rate, there are still many factors needed to be addressed in order to deduce the standard Predicted 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. In the Analysis, the authors have implemented different machine learning algorithms and have achieved better accuracy based on pertinent features of the dataset