Supervisor
Taufique Ahmed
Programme
MSc in Data Analytics
Subject
Computer Science
Abstract
Accurate life expectancy forecasting is essential for health policy planning, yet research comparing statistical, machine learning, and deep learning approaches under real-world constraints remains limited. This study evaluates ARIMA/ARIMAX, tree-based, and neural network models using Irish and global datasets, considering small samples, missing data, and COVID-19 shocks. ARIMAX with lagged socioeconomic variables outperformed LSTM and other ML/DL methods. Income-based stratification improved predictive accuracy and interpretability, with SHAP analysis highlighting GDP per capita for developed countries and school enrolment and trade indicators for developing contexts. Results provide practical guidance for policymakers and establish limits for model complexity under constrained health data.
Date of Award
2025
Full Publication Date
2025
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Rath, E.
(2025) Exploring the Determinants of Life Expectancy at Birth: Predicting and Forecasting Global Health Trends Using Statistical, Machine Learning, and Deep Learning Models. CCT College Dublin.
DOI: https://doi.org/10.63227/652.299.100