Fuzzy Cognitive Map Reasoning Mechanism for Handling Uncertainty and Missing Data: Application in Medical Diagnosis

In this study, the fuzzy causal map inference mechanisms are analyzed for decision making tasks and a comparative analysis is performed to handle with the uncertainty in the problem of pulmonary risk prediction. Fuzzy Cognitive Mapping (FCM) is a causal graphical representation including nodes, determining the most relevant factors of a complex system, and links between these nodes determining the relationships between those factors. It represents knowledge in a symbolic manner and relates states, processes, policies, events, values, and inputs in an analogous manner. In the proposed work, a modified inference mechanism for FCM approach, which handles uncertainty and missing data, is presented and compared with the common fuzzy causal graph reasoning process for a medical diagnosis problem. Through this study, we overcome the problem of missing data and/or incomplete knowledge, especially for the cases where there is no any information about a concept-state or the knowledge of some concepts is insufficient. By this way, the rescaled inference process is proved more reliable, yielding more exact and natural inference results than traditional FCMs. A number of different scenarios for medical diagnosis concentrated on the pulmonary infections are elaborated to demonstrate the functioning of the rescaled FCM inference mechanism.