@article{article_1344862, title={Evaluating Prostate Cancer Diagnosis Using the Adaptive Neural Fuzzy Inference System (ANFIS): A Comparative Analysis of Diagnostic Accuracy}, journal={Turkish Journal of Science and Technology}, volume={20}, pages={583–593}, year={2025}, DOI={10.55525/tjst.1344862}, author={Dalkılıç, Orhan and Demirtaş, Naime and Demirtaş, Abdullah}, keywords={Fuzzy logic, prostate cancer, ANFIS}, abstract={This study explores the application of the Adaptive Neural Fuzzy Inference System (ANFIS) in evaluating prostate cancer diagnosis outcomes. Prostate cancer remains one of the most prevalent cancers among men globally, where early and accurate detection is critical for effective treatment. Despite advancements, diagnosing prostate cancer is inherently complex due to the variability in clinical data and the need for precise interpretation. In this research, ANFIS—a hybrid methodology integrating fuzzy logic and neural networks—was employed to analyze a clinical dataset and develop a diagnostic model. The ANFIS framework excels in handling uncertainty and nonlinear relationships, making it particularly suited for medical diagnostics. The model’s performance was rigorously assessed using multiple evaluation metrics, including accuracy, sensitivity, and specificity. The results demonstrate that ANFIS achieves high diagnostic accuracy, significantly reducing unnecessary biopsies by 45.45% compared to traditional methods. This highlights its potential as a reliable decision-support tool in clinical settings. By leveraging ANFIS, clinicians can enhance diagnostic precision, optimize resource allocation, and improve patient outcomes. The study underscores the transformative role of intelligent systems in advancing prostate cancer management.}, number={2}, publisher={Fırat University}