A Machine Learning Approach to Actuarial Life Table Estimation in Lung Cancer Patients

Abstract

Cancer-related mortalities worldwide are most caused by lung cancer, and one of the major causes of passing worldwide is still cancer. A dangerous disease is lung cancer, which requires accurate survival modelling to assist in actuarial evaluations, public health planning, and clinical decisions. Life expectancy and mortality risk across age groups are calculated using essential tools such as actuarial life tables, but complex real-world data is frequently struggled with by traditional methods. Actuarial life tables for patients with lung cancer are created using a data set of more than 500,000 patient records with 15 key variables from 2014 to 2024 across European countries, employing Extreme Gradient Boost Accelerated Failure Time (XGBoost AFT) based survival analysis. The main objective is to develop agespecific mortality rates and life expectancy for patients with lung cancer. In contrast to earlier research that was reliant on traditional models, the nonlinear learning capabilities of XGBoost AFT models areutilized in this study to allow for more accurate estimation of mortality trends. A data-driven, machine learning approach to actuarial life table development is contributed by this study, with information about lung cancer survival patterns being provided. The understanding of survival trends, treatment planning, efficient use of healthcare resources, and assessment of the results of initiatives is aided by physicians, researchers, and policymakers. Public health initiatives focused on early identification and prevention are also guided, as well as future healthcare requirements being forecast.