Drawing on Uncertainty Methodologies of Neutrosophic Hypersoft Sets in Cognitive Computing-Driven Healthcare Systems

A new paradigm called cognitive computing simulates human reasoning and decision-making through integrating advanced techniques such as artificial intelligence (AI) and natural language processing (NLP). Cognitive computing systems, in contrast to traditional systems, can handle both structured and unstructured data, adjust to new information, and offer context-sensitive insights. This study examines how cognitive computing improves decision-making, personalization, and human-machine collaboration in various fields. Cognitive computing in the healthcare sector processes clinical notes, imaging data, and electronic health records to help physicians with diagnosis, treatment planning, and patient engagement. This study examines key applications, including their role in diagnostic support, where they help clinicians identify diseases, including rare conditions, with increased accuracy. This study aims to evaluate the healthcare systems that deploy cognitive computing systems in its applications. These healthcare systems are formed in this study as alternatives that are evaluated based on a set of criteria. Hence, a soft decision model is constructed to serve the paper’s objectives. Therefore, Multi-Criteria Decision Making (MCDM) techniques are used together with Triangular Neutrosophic Sets (TrNSs) and Hypersoft sets.