Future Evolution of Telemedicine: Enhancing Healthcare Accessibility and Reliability through the Integration of Machine Learning Techniques

Ghat, Arpitha Bhaskara Rao (2023) Future Evolution of Telemedicine: Enhancing Healthcare Accessibility and Reliability through the Integration of Machine Learning Techniques. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (1MB) | Preview
Preview	PDF (Configuration Manual) Download (1MB) | Preview

Abstract

The research investigates the future evolution of telemedicine, focusing on enhancing healthcare accessibility through the integration of machine learning techniques. Amidst the COVID-19 pandemic, a four-week survey in the United States was conducted to analyse the increasing trend in telemedicine usage. Utilizing this dataset, the research applies ARIMA, SARIMA, LSTM, and Bi-LSTM models to forecast telemedicine utilization, with Mean Squared Error (MSE) as the error metric for evaluating predictive accuracy. MSE is pivotal in determining the model's precision in forecasting, measuring the average squared difference between predicted and actual values. The ARIMA model, serving as the baseline, registered a higher MSE of 22.2804, revealing its limitations in handling complex data. The SARIMA model showed improvement, reducing the MSE to 17.7817 and demonstrating better capability in addressing seasonal variations. The LSTM model further advanced accuracy, lowering the MSE to 7.1710, indicating its strength in deciphering intricate data patterns. However, the Bi-LSTM model proved to be the most effective, achieving the lowest MSE of 2.3902, which highlights its exceptional ability in forecasting. This signifies an approximately 89.42% increase in accuracy from ARIMA to Bi-LSTM. These findings illustrate that advanced ML models, especially the Bi-LSTM outperforms the to transform telemedicine into a more efficient medical platform.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Muntean, Cristina Hava UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science R Medicine > R Medicine (General) R Medicine > Healthcare Industry Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	08 May 2025 11:47
Last Modified:	08 May 2025 11:47
URI:	https://norma.ncirl.ie/id/eprint/7516

Actions (login required)

View Item