• Abstract

    The rising prevalence of chronic disorders such as COVID-19 has reopened the worldwide demand for urgent healthcare services. The current pandemic highlights weaknesses in the established healthcare system, demonstrating that hospitals and clinics cannot handle this situation by themselves. One important aspect of technology supporting modern healthcare solutions is intelligent networked wearables. The Internet of Things (IoT) has advanced to the point that these wearables can collect data on an unparalleled scale. Wearable technology is used to collect context-specific data about our behavioral, psychological, and physical well-being. Managing the large amounts of data produced by wearables and other IoT healthcare devices can be difficult and have a detrimental impact on the decision centers ability to make informed decisions. Using big data (BD) analytics to mine and extract information and make predictions and deductions using knowledge has generated a lot of interest. Research in machine learning (ML) has been effective in addressing a range of networking issues, including resource allocation, routing, traffic engineering, and security. The use of ML-based approaches to enhance different IoT applications has increased. Even though ML and BD analytics have been deeply investigated, most of it talks concerning the way ML-based BD analysis approach is developing in the context of IoT healthcare. In this study, the Analysis of BD using ML techniques in the healthcare industry has been extensively discussed. The advantages and disadvantages of current methods are also discussed, along with a number of research challenges. Our research will help government organizations and healthcare professionals stay up to date on the most recent developments in ML-based data analysis for intelligent healthcare.

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How to cite

Sharma, S., Saxena, K., Reddy, B. R. S., & Singh, N. (2024). Towards intelligent healthcare: Investigating IoT, Big Data, and AI Collaboration. Multidisciplinary Reviews, 6, 2023ss058. https://doi.org/10.31893/multirev.2023ss058
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