Big Data Analytics in IoT-Enabled Smart Healthcare Systems: A Machine Learning Survey - Nigam, Charul; Sharma, Priti; Anand, Rahul V.; Uttam, Arun Kumar (2024 International Conference on Communication, Computing and Energy Efficient Technologies (I3CEET), 2024)
The global epidemic of chronic illnesses, such as the COVID-19 pandemic, underscores the pressing need for readily available healthcare services. The flaws in the traditional healthcare systems that are centered on hospitals and clinics are no longer hard to recognize. Wearables that are connected and intelligent have thus emerged as a critical technological solution, leveraging advancements in the realm of the Internet of Issues. These Internet of Things (IoT)-enabled wearables collect a lot of data and provide context-specific behavioral, psychological, and physical health insights. The volume of data generated by wearables and other IoT healthcare devices is a major obstacle to proper management and may impede decision-making processes. Big data analytics is becoming a more and more common way to get valuable data and enable predictive analysis in order to get around this problem. Furthermore, machine learning (ML) has been used to a range of networking issues, such as resource allocation, routing, traffic engineering, and security. Despite the abundance of research on big data analytics and machine learning, there is a lack of specialized studies on the development of machine learning techniques for large-scale data analysis in the Internet of Things healthcare space. This paper offers a comprehensive analysis with a focus on big data analysis in the healthcare sector using machine learning techniques. It discusses a number of research questions and evaluates existing approaches, including the benefits and drawbacks of each. This project aims to educate government agencies and healthcare professionals on the latest advancements in machine learning (ML)-based data analytics for cognitive healthcare systems..