A novel framework for multiple disease prediction in telemedicine systems using deep learning

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

Disclosure statement

No potential conflict of interest was reported by the author(s).