Book Title: Transformative Approaches in Multidisciplinary Research (TAMR)
Chief Editors: Dr. Meenakshi Kujur, and Dr. Hamidun Bunawan
Associate Editors: Mr. Gunjit Singhal, and Dr. Asma Farooque
Co-Editors: Dr. Suresh Kamarapu, and Dr. Souvik Sur
Chapter: 1
DOI: https://doi.org/10.59646/672/1
Authors: Asiya Sarkhawas, and Prem Ovhal
Abstract
The use of Internet of Things (IoT) and Machine Learning (ML) is changing the way healthcare systems work. Earlier, healthcare mainly focused on treating diseases after they occurred, but now it is moving towards early detection and preventive care. IoT devices such as smartwatches, fitness bands, and connected medical equipment help in collecting real-time health data like heart rate, activity level, and other vital signs. This data is then analyzed using machine learning techniques to identify patterns and predict possible health issues. This paper studies how the combination of IoT and ML is used in smart healthcare systems. It focuses on important applications such as remote patient monitoring, virtual healthcare services (hospital-at-home), and smart systems for elderly care. These technologies help in improving patient care, reducing hospital visits, and providing faster and more accurate medical decisions. However, there are several challenges in implementing these systems. Security and privacy of patient data is a major concern, as IoT devices are connected to the internet and can be vulnerable to attacks. Another issue is the lack of proper data sharing between different healthcare systems. Also, machine learning models are sometimes difficult to understand, which can reduce trust among doctors. This study is based on analysis of existing systems and real-life examples to understand both the benefits and limitations of using IoT and ML in healthcare. The paper concludes that while these technologies have great potential to improve healthcare services, it is important to solve issues related to security, data sharing, and system reliability for better adoption in the future.
Keywords: IoT, Machine Learning, Smart Healthcare, Data Security, Predictive Analysis
References
- D. B. Rawat, C. Bajracharya, “Cybersecurity for Smart Healthcare Systems: Challenges and Opportunities,” IEEE Communications Magazine, vol. 57, no. 4, pp. 74–80, 2019.
- M. Chen, Y. Ma, J. Song, C. F. Lai, B. Hu, “Smart Clothing: Connecting Human with Clouds and Big Data for Sustainable Health Monitoring,” Mobile Networks and Applications, vol. 21, no. 5, pp. 825–845, 2016.
- S. Patel, H. Park, P. Bonato, L. Chan, M. Rodgers, “A Review of Wearable Sensors and Systems with Application in Rehabilitation,” Journal of NeuroEngineering and Rehabilitation, vol. 9, no. 21, 2012.
- A. H. Sodhro, S. Pirbhulal, V. H. C. de Albuquerque, “Artificial Intelligence Driven Mechanism for Edge Computing Based Industrial Applications,” IEEE Transactions on Industrial Informatics, vol. 15, no. 6, pp. 3588–3596, 2019.
- J. Wan, M. A. Imran, “Wearable IoT for Healthcare: A Survey,” IEEE Access, vol. 6, pp. 69100–69117, 2018.
- E. Jovanov, A. Milenkovic, “Body Area Networks for Ubiquitous Healthcare Applications:Opportunities and Challenges,” Journal of Medical Systems, vol. 35, no. 5, pp. 1245–1254, 2011.
- K. R. Choo, “The Cyber Threat Landscape: Challenges and Future Research Directions,” Computers & Security, vol. 30, no. 8, pp. 719–731, 2011.
- P. Gope, T. Hwang, “BSN-Care: A Secure IoT-Based Modern Healthcare System Using Body Sensor Network,” IEEE Sensors Journal , vol. 16, no. 5, pp. 1368–1376, 2016.