Traditional Vs Modern Approaches to Life Expectancy Prediction

Demographers, planners in health care, and actuaries have long depended on life expectancy predictions. This paper compares traditional methods, such as Lee-Carter and Gompertz-Makeham, with new frameworks, such as those developed by the Big Life Project, the Global Burden of Disease, and the UK Biobank, through machine learning. Traditional models are noted for being easy to understand, reliable over time, and familiar to most economists. On the other hand, they are getting less able to adjust to the many dimensions, individuality, and changing aspects of the current health challenges patients face. These current strategies use big data from biomedical fields, AI, and new technologies to individually predict how long patients may live. This information is used in these models to help them make more flexible and appropriate predictions. In addition to making medical care safer and more precise, modern models bring difficulties linked to bias in algorithms, problems with understanding their results, protection of patient data, and the necessary computing power. This paper compares different paradigms using several measures, pointing out their advantages and disadvantages, as well as the areas in which they overlap. Combining the old demographic approach with the predictive power of machine learning could be the best direction for analysis. The article highlights that to overcome the ethical, technical, and broader usage issues of current models, it is important to properly adapt them, make their design clear, and involve everyone in data management. When life expectancy prediction is handled sensibly and offered to all fairly, it could revolutionize health care by helping us see important trends, improve health policies, assist doctors in making choices, and guide people to understand their future.