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| Title | Digital Twin For Iot Healthcare System: A Blood Pressure Prediction With Ml Classifier |
|---|---|
| ID_Doc | 20194 |
| Authors | Anand M.; Babu S. |
| Year | 2025 |
| Published | Digital Twins for Sustainable Healthcare in the Metaverse |
| DOI | http://dx.doi.org/10.4018/979-8-3693-4199-5.ch006 |
| Abstract | In the era of Industry 4.0, smart linked hospitals have emerged as essential elements and self- contained ecosystems of smart cities with the introduction of the Internet of Things (IoT). One of the most complicated and quickly evolving IoT application sectors is healthcare. In addition, the healthcare industry has always embraced new technology and tried for opportunities on it. Healthcare systems can now use analytics over IoT data streams as a collection of data to find new information, predict early detection, and support decisions on crucial situations for the improvement of life quality. Hospital records, patient medical records that do not contain sensitive information, the results of medical examinations, and Internet of Things applications are some of the sources of big data in the health sector. Because of this, the massive stream of unstructured data is too much for the existing approaches to handle effectively. Digital twin (DT) is novel and promising technology in this field. It is believed that DT will revolutionize the idea of digital healthcare and carry this business to levels that were never before attained. DT is a real- time transformed data virtual replica of a physical item that reveals its present status. A virtual patient replica might be the best way for healthcare institutions to promote health, give patients more control over their health, and enhance operational efficiency. By means of this simulation, the current health status of the patient will be monitored. On additional to that, developments for the future can be predicted using medical history. This book utilizes the DT framework to propose and develop an IOT healthcare system. As a result, a machine learning- based blood pressure approach was developed to identify cardiac issues and diagnose heart disease. The models that have been implemented into practice were able to accurately predict a specific cardiac condition using a variety of techniques. The gathered data has shown that incorporating DT into the healthcare industry would enhance procedures by uniting patients and medical staff in a scalable, intelligent, and encompassing health ecosystem. Additionally, the concept for using machine learning and artificial intelligence with various human body parameters for continuous monitoring and abnormality detection came from the implementation of a decision tree classifier that identifies pressure level. This framework is a useful addition to enhance healthcare operations and digital healthcare. By compiling the vast amount of data on the technology used to create DT in healthcare, this study closes the gaps. The characteristics of DT, communication tools and technologies used in the development of DT models, standards, reference models, and the researcher's most recent work in the healthcare industry are among the main topics of this paper. This book's primary goal is to present an approach for organizing the manipulation of secure and dependable data in the context of health care, with a particular focus on the development of digital twins for individualized treatment. Both types of experts will use these: (i) data analysts, who will create expert recommender systems and extract information (explainable AI); and (ii) medical practitioners, who will use the knowledge produced by their study to improve diagnosis. The knowledge generated by their study will be used by medical experts to better diagnosis, as well as data analysts who will develop expert recommender systems and extract information. © 2025, IGI Global Scientific Publishing. All rights reserved. |
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