Hierarchical medical diagnosis approach for COVID-19 based on picture fuzzy fairly aggregation operators

Abstract

In Pakistan, a hierarchical healthcare system is an efficient way of addressing the issue of limited and insufficient healthcare services. Identifying the various degrees of disease based on the doctor’s diagnosis is an important step in developing the hierarchical healthcare treatment structure. This research presents a framework for dealing with the issue of diagnosis values presented as “picture fuzzy numbers (PFNs)”. Specifically, the goal of this study is to establish some innovative operational laws and “aggregation operators” (AOs) in a picture fuzzy environment. In this regard, we proposed some new neutral or fair operational laws that incorporate the concept of proportional distribution in order to achieve a neutral or fair remedy to the positive, neutral and negative aspects of PFNs. Based on the developed operational laws, we proposed the “picture fuzzy fairly weighted average operator” and the “picture fuzzy fairly ordered weighted averaging operator”. Compared to previous techniques, the proposed AOs provide more generalized and reliable. Furthermore, using proposed AOs with multiple decision-makers and partial weight information under PFNs, a “multi-criteria decision-making” algorithm is developed. Finally, we provide an example to show how the novel approach can aid hierarchical treatment systems. This is essential for merging the healthcare capabilities of the general public and optimizing the medical care system’s service performance.