Modeling distress in cancer patients using a fuzzy logic approach based on expert consensus

Abstract Objective To develop and validate a fuzzy logic model based on expert consensus to elucidate distress dynamics in cancer patients, examining the non-linear interactions between psychological, social, and medical factors. Methods A two-round Delphi process with 23 psychosocial oncology experts was conducted to generate an interaction matrix of 18 distress-related variables. Using the skfuzzy Python library, a Mamdani fuzzy inference system was constructed, focusing on Negative Psychological Factors, Symptoms, and Positive Psychological Factors as primary drivers. Model validation included network analysis, time-series simulations, and sensitivity analyses, compared against a traditional crisp system dynamics model. Results The fuzzy model confirmed a self-reinforcing “vicious cycle” of distress driven by Negative Psychological Factors (weight = 2.00) and Symptoms (weight = 1.50). Simulations demonstrated that positive psychological interventions could reduce overall distress levels by up to 25%. Network analysis identified distress as a central system hub, while the fuzzy model produced smoother, more clinically realistic trajectories than the crisp model. Conclusion This study provides a robust mathematical explanation for the success of the validated DIC-2 clinical tool. The results underscore the necessity of early, multidisciplinary interventions to disrupt distress cycles, supporting the clinical shift toward treating distress as the “sixth vital sign”.