9th International Conference on ICT for Sustainable Development

Authors - Savita S. Hanji, Mahantesh N. Birje
Abstract - The rise in popularity of IoT-aided healthcare systems within distributed environments has facilitated global access to personalized and distributed electronic health services. These systems, known as Internet of Things- based distributed healthcare systems (IoT-DHS), leverage the Internet of Things (IoT) to connect diverse medical resources. Consequently, they offer intelligent, reliable, and effective healthcare services, particularly to patients with chronic illnesses and the elderly. An essential aspect of these systems is the analysis of healthcare data, which plays a pivotal role in informed decision-making regarding disease prediction, medication prescriptions, and overall patient health maintenance. However, healthcare data collected from different systems often contains numerous missing values. These missing values can introduce bias, compromise decision accuracy, and diminish the ability to detect critical associations, posing significant challenges across various fields. To address this issue, state-of-the-art methods for handling missing data in IoT-DHSs have been surveyed. From this survey, a taxonomy has been derived, categorizing these methods based on different aspects of IoT-DHSs.