An Intelligent IoT Framework for Personalized Healthcare: Integrating Machine Learning Algorithms for Real-time Patient Monitoring and Diagnosis
Keywords:
IoT, Machine Learning, Patient Monitoring, Healthcare, Personalized CareAbstract
The Internet of Things (IoT) has changed healthcare by letting doctors keep an eye on patients in real time and give them more personalized care. We present an Intelligent IoT Framework for Personalized Healthcare (IIFPH) in this study. It includes machine learning (ML) methods for diagnosing and tracking patients in real time. IoT devices, like medical tools and monitors that can be worn, are used by the framework to constantly gather data about patients. The information is then sent to a central computer site to be analyzed and processed. The IIFPH uses machine learning techniques, such as deep learning and ensemble methods, to look at the data and figure out how healthy the patient is. To make sure the results are accurate and reliable, these algorithms are taught using a wide range of datasets. The structure also has a unique suggestion system that gives each patient individualized medical care based on their health history and personality. Its ability to provide real-time tracking and analysis is one of its most important features. This lets healthcare workers act quickly if anything goes wrong. Encryption methods and access rules built into the system also protect data privacy and security. To find out how well the proposed framework worked, we used real patient data in a number of studies. The data show that the IIFPH can correctly track and identify a wide range of health problems, from short-term illnesses to long-term ones. The suggested approach could, in general, improve the standard of healthcare services and the results for patients in personalized healthcare situations.
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