Machine learning based prediction of life expectancy
Date
2022
Authors
Lipesa, Brian Aholi
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Journal ISSN
Volume Title
Publisher
Strathmore University
Abstract
The social and financial systems of many nations throughout the world are significantly impacted by life expectancy (LE) models. Numerous studies have pointed out the crucial effects that life expectancy projections will have on societal issues and the administration of the global healthcare system. These approaches offer a variety of strategies to enhance society-related advanced care planning and healthcare. Over time, research has proven that the vast majority of the existing factors were insufficient to forecast the lifespan of the general population. An understanding of the chosen sampling population’s death rate served as the foundation for earlier models. Researchers have asserted that despite improvements in forecasting approaches and meticulous work in the past, there are still several elements that must be taken into account to determine life expectancy rates in addition to death rates. As a result, life expectancy research now includes a broader focus on issues related to education, health, the economy, and social welfare. In this study, the author developed a model for estimating life expectancy rates taking into consideration health, socioeconomic, and behavioral characteristics by using the eXtreme Gradient Boosting (XGBoost) algorithm to data from 193 UN member states. The effectiveness of the model’s prediction was compared to that of the Random Forest (RF) and Artificial Neural Network (ANN) regressors utilized in earlier research. XGBoost attained an MAE and an RMSE of 1.554 and 2.402, respectively. It outperformed the RF and ANN models that achieved MAE and RMSE values of 7.938 and 11.304, and 3.86 and 5.002, respectively. The overall results of this study support XGBoost as a reliable and efficient model for estimating life expectancy.
Description
Submitted in partial fulfilment of the requirements for the degree of Master of Science in statistical science at Strathmore University