/Predicting-Life-Expectancy

A Comprehensive Statistical Analysis of Health Indicators and Time Series Forecasting

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This research paper provides an analysis of data and a predictive modeling approach to better understand and predict life expectancy. By using a dataset this study initially explores health indicators that are closely related to life expectancy. It then applies regression models to examine how variables such, as education, income composition and adult mortality impact life expectancy. Moreover the paper introduces innovative time series forecasting techniques by utilizing ARIMA models to predict trends in life expectancy. The findings offer insights into health planning and policy development by highlighting the diverse factors that influence life expectancy and showcasing the potential of statistical models, in predicting health outcomes. Theoretical Framework:Our research is based on the understanding that life expectancy as a measure of health is influenced by factors. These factors include the quality of healthcare, socioeconomic status, environmental conditions and lifestyle choices. Theoretical models in epidemiology and public health suggest that changes in these factors can greatly impact life expectancy. This perspective aligns with the framework of determinants of health which emphasizes the interconnectedness between health outcomes and social, economic and environmental factors. Conclusion:Our research presents evidence that the duration of life is affected by a combination of various factors, including socioeconomic status, health condition and demographic aspects. Through the utilization of methods such as regression analysis and time series forecasting we have emphasized the substantial influence of education, income structure and health indicators, on life expectancy. These discoveries highlight the significance of public health strategies that not address medical requirements but also take into account broader societal determinants impacting health outcomes.