Original scientific paper
https://doi.org/10.1080/00051144.2024.2301889
A novel framework for multiple disease prediction in telemedicine systems using deep learning
Divya R. Unnithan
; Department of Computer Applications, Noorul Islam Centre for Higher Education, Kumarakovil, India
*
J. R. Jeba
; Department of Computer Applications, Noorul Islam Centre for Higher Education, Kumarakovil, India
* Corresponding author.
Abstract
Telemedicine systems are gaining popularity due to their ability to provide remote medical
services. These systems produce a lot of data, which may be used for a variety of purposes,
including quality improvement, decision-making, and predictive analytics. Deep learning is an
effective data mining method that may be applied to this data to bring out significant findings. Telemedicine systems, which allow patients to receive medical consultation and treatment
remotely, generate vast amounts of data. Analyzing this data can provide valuable insights for
improving patient care and optimizing the telemedicine system. Data mining techniques can be
incredibly valuable for telemedicine systems, as they can help to identify patterns and insights in
large amounts of patient data. Data mining techniques can assist telemedicine systems in making
better decisions and offer better care to patients. In this paper, a novel framework for multiple disease prediction in telemedicine system using an effective deep learning algorithm was
developed. The proposed multiple disease prediction system is composed of Long Short Term
Memory (LSTM) unit.The experimental results revealed that the suggested disease prediction
model exceeded the present models with an accuracy of 98.51%.
Keywords
Telehealth system; telemedicine system; data mining techniques; clustering; clustering techniques; classification techniques
Hrčak ID:
326085
URI
Publication date:
26.2.2024.
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