2021
DOI: 10.3390/bdcc5040051
|View full text |Cite
|
Sign up to set email alerts
|

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… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
2

Citation Types

0
5
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
5
1

Relationship

0
6

Authors

Journals

citations
Cited by 9 publications
(5 citation statements)
references
References 26 publications
(27 reference statements)
0
5
0
Order By: Relevance
“…From 2010 to 2015, the average annual proportion of individuals aged 65 and above in the study area was 10.4% but rose to an average of 13.4% from 2016 to 2019. From 2010 to 2015, those aged 75 years and above formed 2.1% of the study population, whereas from 2016 to 2019, this group of individuals formed 2.7% of the population 54. Furthermore, the incidence of AMI rose from 324.0 per 100 000 population in 2016 to 354.4 per 10 000 population in 2017, a substantial rise compared those in the years proximate to that 19.…”
Section: Discussionmentioning
confidence: 93%
See 1 more Smart Citation
“…From 2010 to 2015, the average annual proportion of individuals aged 65 and above in the study area was 10.4% but rose to an average of 13.4% from 2016 to 2019. From 2010 to 2015, those aged 75 years and above formed 2.1% of the study population, whereas from 2016 to 2019, this group of individuals formed 2.7% of the population 54. Furthermore, the incidence of AMI rose from 324.0 per 100 000 population in 2016 to 354.4 per 10 000 population in 2017, a substantial rise compared those in the years proximate to that 19.…”
Section: Discussionmentioning
confidence: 93%
“…From 2010 to 2015, those aged 75 years and above formed 2.1% of the study population, whereas from 2016 to 2019, this group of individuals formed 2.7% of the population. 54 Furthermore, the incidence of AMI rose from 324.0 per 100 000 population in 2016 to 354.4 per 10 000 population in 2017, a substantial rise compared those in the years proximate to that. 19 A new blood biomarker, high sensitivity troponin, was rolled out to hospitals for AMI detection from 2014.…”
Section: Discussionmentioning
confidence: 98%
“…The Singaporean government has taken diverse approaches to provide better care for older people. For instance, the government is on the verge of increasing the number of nursing home beds to 14,000, accounting for a 50% increase from the report in 2010 [ 9 ]. These measures were based on the expected increase in demand for beds as the aging population continues a rising trend [ 6 ].…”
Section: Introductionmentioning
confidence: 99%
“…Finally, it can help enterprises increase the value, mission, and career life of insurance practitioners, and create a win-win-win situation for clients, companies, and individuals. Examples of past studies on data mining and long-term care insurance include the following review: research on government pension initiatives and their possible impact on an aging population [3], association between family loss and cognitive impairment [4], innovative insurance services in cloud computing [5], discussing customer churn for life insurance policies [6], and more. Although substantial studies have been performed on long-term care insurance, those further exploring long-term care insurance businesses through the behavior of customers purchasing insurance products are still critically lacking.…”
Section: Introductionmentioning
confidence: 99%
“…The data and rules have been summarized by modeling the prediction accuracy and producing the decision tree graph. The research objectives can be summarized as follows: (1) to establish a classification prediction model, and find the most suitable classification model through data mining technology; (2) to find the best classifier through empirical study; (3) to find the rules and important key factors influencing the willingness to buy the long-term care insurance through the Decision Tree graph.…”
Section: Introductionmentioning
confidence: 99%