A Study on Singapore’s Ageing Population in the Context of Eldercare Initiatives Using Machine Learning Algorithms

Abstract

Ageing has always directly impacted the healthcare systems and, more specifically, the eldercare costs, as initiatives related to eldercare need to be addressed beyond the regular healthcare costs. This study aims to examine the general issues of eldercare in the Singapore context, as the population of the country is ageing rapidly. The main objective of the study is to examine the eldercare initiatives of the government and their likely impact on the ageing population. The methodology adopted in this study is Cross-Industry Standard Process for Data Mining (CRISP-DM). Reviews related to the impact of an ageing population on healthcare systems in the context of eldercare initiatives were studied. Analysis methods include correlation and machine learning algorithms, such as Decision Tree, Logistic Regression and Receiver Operating Characteristics curve analysis. Suggestions have been provided for various healthcare and eldercare systems’ initiatives and needs that are required to transform to cope with the ageing population.