A computational diagnostic model for infectious diseases via similarity measures on N-framed plithogenic hypersoft sets

In healthcare, tracking evolving symptoms is a tough task. Symptoms do not stay fixed. They shift, overlap, and change over time, often leaving clinicians unsure of what is happening. Imagine a patient with a fever that spikes one day, drops the next, and then climbs again. Is it malaria? Dengue? Or maybe both? Sorting this out can feel like chasing a moving target. Our research offers a mathematical solution by formulating distance and similarity measures on N-framed plithogenic hypersoft sets. This methodology takes a patient’s symptoms data and splits it into timed snapshots like frames in a film. By comparing these snapshots to known disease profiles, such as malaria or dengue, we can measure how closely a patients symptoms match each condition at different stages. This approach not only identifies what is present but also tracks how it is evolving, even when multiple diseases overlap. Beyond healthcare, this framework has wider potential. It can adapt to areas like AI-driven analytics or infrastructure monitoring anywhere shifting patterns in uncertain systems need to be understood. By measuring and visualizing change over time, this research boosts diagnostic accuracy in medicine and opens new ways to tackle dynamic data across fields.