NORMA eResearch @NCI Library

A machine learning prediction-based analysis for the implementation of general practitioner E-health and Fintech services in Ireland

Pillay Rajagopalan, Reena (2020) A machine learning prediction-based analysis for the implementation of general practitioner E-health and Fintech services in Ireland. Masters thesis, Dublin, National College of Ireland.

[img]
Preview
PDF (Master of Science)
Download (1MB) | Preview
[img]
Preview
PDF (Configuration manual)
Download (368kB) | Preview

Abstract

The public health service in Ireland has been facing ongoing pressure in dealing with patient waiting lists. The wait times to see a general practitioner also contributes to the overall waiting list statistics of Ireland. This paper conducts a quantitative analysis of Dublin and County Meath by analysing the population, number of GP available, GP visitation lists, unemployed age groups, smart gadget and broadband users based on recent Census, Data.gov.ie and HSE 2016 data. The purpose of this study is to provide quantitative insights on the existing general practitioner systems and the likelihood of implementing e-health and fintech services. CRISP-DM framework was applied to structure the research methodology and to apply machine learning techniques accordingly. In conducting this study; Correlation, Naïve Bayes, Data Mining and Visualisation tools were selected to gather information on the demographics and the tech savvy environment of both counties. About 12% of the population belonging to 16 to 24 and 25 to 34 age group were unemployed in both counties indicating the higher dependency for public healthcare. The tech savvy analysis shows the population of Dublin has 78% smart gadget and broadband users and Meath has 22%. These quantitative results reveal that while there are many factors contributing to the healthcare, there is a 75% likelihood of E-health and Fintech services being implemented in Dublin and County Meath to achieve a more efficient and effective General Practitioner system.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software

R Medicine > Healthcare Industry
Divisions: School of Computing > Master of Science in FinTech
Depositing User: Dan English
Date Deposited: 29 Jan 2021 17:25
Last Modified: 29 Jan 2021 17:25
URI: http://norma.ncirl.ie/id/eprint/4576

Actions (login required)

View Item View Item