An algebraic modeling for tuberculosis disease prognosis and proposed potential treatment methods using fuzzy hypersoft mappings

Abstract

This study aimed to put forward an Avant-guard mathematical model for assisting the diagnostic process of this Mycobacterium (Tuberculosis (TB) bacterium) based on a novel adaptive fuzzy like structure. It is tough to ascertain the specific type of TB from its seriousness after looking at the symptoms as they overlap with numerous other respiratory infections. This structure, i.e., the fuzzy hypersoft set (FHS), extends the fuzzy soft set used to resolve this issue. The FHS is a more generalized, flexible and reliable algebraic model which is capable of managing the shortcomings of existing fuzzy soft set-like models with the entitlement of multi argument based domain for the approximation of TB patients (alternatives) under examination. It tackles the uncertain observations of medical experts for the approximation of patients with the help of fuzzy membership grade within [0,1]. When the measurements possess sub-values, it is problematic to see refinement in the patient's progression timelines and anticipate the prescription term in a clinical appraisal. This novel fuzzy-like theory categorizes the distinct attributes into corresponding disjoint attribute-valued sets for better interpretation. It is difficult to distinguish a single upper-respiratory infection due to the sheer number of infections that influence the lungs and associated breathing organs. This investigation involves monitoring and constructing a bridge between the presented symptoms and the treatment given to the patient. The FHS-mapping will recognize the severity of the disease and the proposition of adequate treatment for the patient. The presented structure can prove to be an excellent diagnosis aiding tool as it has infinite analysis potential with mathematical interfacing with the patient's condition with time.