A Comparative Analysis of Mamdani and Sugeno Fuzzy Controllers in Risk Evaluation of Gastric and Prostate Cancer Using Fuzzy Soft Sets

Abstract

Accurate and early cancer risk evaluation is vital for improving patient outcomes, yet conventional diagnostic models often struggle with the inherent vagueness and imprecision of clinical data. This research presents a novel comparative analysis of Mamdani and Sugeno fuzzy inference systems (FIS) integrated with fuzzy soft set theory to assess the risk levels of gastric and prostate cancers. Patient records comprising linguistic and numerical variables—such as age, PSA levels, gastrointestinal symptoms, and tumor biomarkers (CEA, CA19-9, PCA3)—were modeled using fuzzy logic frameworks. The fuzzy soft set approach enabled flexible, parameter-driven uncertainty modeling, making the risk classification process more adaptive and tolerant to incomplete or noisy data. A comprehensive evaluation of both controllers revealed that the Sugeno model demonstrated superior performance in terms of accuracy (91.3%), computational speed, and Area Under the Curve (AUC = 0.94), while the Mamdani model provided enhanced interpretability of decision rules, making it more suitable for clinician-centered applications. This work underscores the effectiveness of mathematically grounded, hybrid fuzzy-soft frameworks in clinical decision support and presents a robust alternative to rigid threshold-based diagnostic tools.