Adaptive Fuzzy Logic–Based Diagnostic Model for Early Detection of Pancreatic Cancer

Abstract

Pancreatic cancer remains one of the most lethal malignancies due to its silent progression and the lack of reliable methods for early detection. Conventional diagnostic techniques, including CA 19-9 biomarker analysis and imaging modalities such as CT and MRI, often fail to identify early-stage tumors because of limited sensitivity, specificity, and their inability to manage imprecise clinical presentations. This study proposes an Adaptive Fuzzy Logic-Based Diagnostic Model designed to improve early pancreatic cancer detection by integrating heterogeneous data sources, including clinical symptoms, biochemical markers, genetic profiles, and imaging findings. The model employs an adaptive fuzzy inference system capable of interpreting uncertain and ambiguous medical data, enabling a refined risk stratification process.

Using a dataset constructed from hypothetical but clinically consistent patient records, the model was trained and validated through optimized fuzzy rule sets and membership functions. Performance evaluation demonstrated significantly enhanced diagnostic accuracy, achieving 92% sensitivity and 89% specificity, outperforming conventional approaches that averaged 75% and 70%, respectively. The model also achieved an AUC of 0.94, indicating superior discriminative capability in distinguishing high-risk from low-risk cases. These results highlight the model’s potential as a clinical decision-support tool capable of improving early diagnosis, reducing misclassification, and supporting timely interventions. The study underscores the value of integrating fuzzy logic with multi-modal medical data and provides a framework for future AI-driven diagnostic advancements in oncology.