Abstract
<jats:p>Diagnostic accuracy and efficiency of pneumonia remain one of the major challenges in clinical decision-making due to the diverse symptoms, variable patient response to treatment, and ambiguous medical data. In order to overcome those problems, this chapter presents a hybrid fuzzy Multi-Criteria Decision-Making (MCDM) framework that determines Fuzzy Analytical Hierarchy Process (FAHP) alongside Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS). Our innovative methodology analysis of key diagnosis criteria on shortness of breath, chest X-ray, blood, sputum culture, and immunization status on the uncertainty basis through hesitant assessment and linguistic assessment. FAHP is used to calculate the weights of trustworthy criteria through paired comparisons, which are fuzzy and attempt to achieve consensus among two or more decision makers. Fuzzy TOPSIS, in its turn, ranks the diagnosis options in terms of proximity to the ideal profile of the physician. The hybrid method utilizes the capabilities of FAHP and Fuzzy TOPSIS to enhance the robustness, reduce the ambiguity in the clinical judgment of the physician, and streamline the prioritization of the diagnosis to doctors. Particularly, experimental examination indicates that the integrated model offers consistent, clarified, and probable decisions aiding in diagnosing pneumonia. The chapter proves fuzzy MCDM as having a potential to develop the intelligent healthcare system, particularly when medical information is vague and incomplete.</jats:p>