The Design of new Way of Fuzzy Trees for the Implementation of Strategic Plan of Medical System for the Personalised Care

Reliability stands as a vital feature in varied systems, particularly in healthcare systems. These systems need specific mathematical models because reliability engineering views them as complicated and unpredictable. Although the structure function accurately captures actual systems, its use in the context of partial or uncertain data is hampered by current deterministic approaches. This research uses fuzzy decision structures (FDTs) to evaluate incomplete data and suggests a novel method for building the structural function. Here, input and output characteristics are understood as values from the structure function and as component states, respectively. The reliability study of a system of health care, namely the four-component, 36-state vector laparoscopic surgical system, serves as an example of the technique. Furthermore, three benchmark systems with 243, 108, et 512 state vectors in respectively, are used to evaluate the suggested approach. When state vectors are randomly removed (from 5% to 90%) prior to the generation of the structure function, uncertainty is introduced. For each of the systems that utilize a 50% deletions rate, the error rate stays below 0.2%. The research concludes that handling missing data in health care facilities is a good fit for reliability analysis based on FDT.