An initiative in medical diagnosis for detecting the factors of hypoglycemia disease with a new approach of interval-valued Pythagorean fuzzy linear Diophantine Aczel Alsina aggregation operators

Abstract

Hypoglycemia is a situation when blood sugar levels drop below normal. It is most commonly associated with Diabetes, particularly in individuals who are taking insulin or other medications that increase insulin secretion. However, it can also occur in people without Diabetes due to various factors like fasting, alcohol consumption, or certain medical conditions. Our main goal is to create a path to calculate the ranking of the most risky factor that can cause Hypoglycemia in the human body. By adopting interval-based fuzzy logic and preserving the Pythagorean constraints, interval-valued Pythagorean fuzzy sets (IVPyFS) provide an effective method for modeling and resolving the issues that involve ambiguity, uncertainty, and incomplete information. The IVPyFS allows the experts to describe their opinions independently using the degree of membership (MD) and non-membership (NMD). We use the Aczel-Alsina operations to enhance the flexibility when the information is obtained to detect hypoglycemia. Consequently, we created a new idea of interval-valued Pythagorean fuzzy (IVPyF) linear Diophantine set (IVPyFLDS). We have formed the weighted average and geometric operators using Aczel-Alsina triangular norms. We investigate some fundamental properties of the developed operators. Furthermore, we observe the sensitivity of the results and compare the results for the justification.