A system of remote patients' monitoring and alerting using the machine learning technique

• Autores: Alanya Beltrán, Joel Elvis, Dhinakaran, M., Phasinam, Khongdet, Srivastava, Kingshuk, Vijendra Babu, D., Kumar Singh, Sitesh
• Formato: artículo
• Fecha de Publicación: 2022
• Institución: Universidad Tecnológica del Perú
• Repositorio: UTP-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.utp.edu.pe:20.500.12867/5788
• Enlace del recurso: https://hdl.handle.net/20.500.12867/5788; https://doi.org/10.1155/2022/6274092
• Nivel de acceso: acceso abierto
• Materia: Machine learning; Patient care; https://purl.org/pe-repo/ocde/ford#3.00.00

Machine learning has become an essential tool in daily life, or we can say it is a powerful tool in the majority of areas that we wish to optimize. Machine learning is being used to create techniques that can learn from labelled or unlabeled information, as well as learn from their surroundings. Machine learning is utilized in various areas, but mainly in the healthcare industry, where it provides significant advantages via appropriate decision and prediction methods. ,e proposed work introduces a remote system that can continuously monitor the patient and can produce an alert whenever necessary. ,e proposed methodology makes use of different machine learning algorithms along with cloud computing for continuous data storage. Over the years, these technologies have resulted in significant advancements in the healthcare industry. Medical professionals utilize machine learning tools and methods to analyse medical data in order to detect hazards and offer appropriate diagnosis and treatment. ,e scope of remote healthcare includes anything from tracking chronically sick patients, elderly people, preterm children, and accident victims.The current study explores the machine learning technologies’ capability of monitoring remote patients and alerts their current condition through the remote system. New advances in contactless observation demonstrate that it is only necessary for the patient to be present within a few meters of the sensors for them to work. Sensors connected to the body and environmental sensors connected to the surroundings are examples of the technology available.