Performance of AI-based healthcare projects with decision algorithm and spherical fuzzy hypersoft environments

Artificial intelligence plays a transformative role in healthcare by integrating multiple functions across the medical workflow, from early disease detection and imaging analysis to personalized treatment recommendations and remote patient monitoring. In medical diagnosis, uncertainty is a major challenge due to incomplete patient information, ambiguous symptoms, and subjective clinical judgment. To address such uncertainty in medical treatment, we propose a novel approach, spherical fuzzy hypersoft sets (SFHySSs), with flexible properties and principles. In addition to a theoretical overview of spherical fuzzy hypersoft (SFHyS) information, flexible operations of Sugeno-Weber triangular norms are also formulated. We also developed an innovative family of mathematical models, namely operators. An intelligent decision-making approach based on multi-criteria decision-making (MCDM) is initiated, incorporating SFHyS information. Additionally, an experimental case study is conducted to demonstrate AI-based healthcare projects by integrating different conflicting criteria and aggregation operators. The sensitivity analysis and comparison approach also demonstrate the reliability of the existing mathematical terminology. Furthermore, some remarkable comments are also discussed.