Diabetes Classification Using SMOTE-Based Intuitionistic Fuzzy K-NN with Weighted HIAD - Tio Valent Novi Yanti Sianturi; Raden Sulaiman (Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang, 2026)
Diabetes risk classification is challenged by class imbalance and diagnostic uncertainty. This study investigates an Intuitionistic Fuzzy Set framework integrated with a proposed Hausdorff-Inspired Attribute Distance within the K-Nearest Neighbor algorithm. The framework models patient profiles using membership, non-membership, and hesitation components to capture clinical ambiguity. We evaluated this approach on the Pima Indians Diabetes dataset using Stratified 5-Fold Cross-Validation, incorporating class balancing and clinically informed feature weighting. Results showed that while a conventional Euclidean-based model achieved the highest accuracy (73.96%) and data balancing maximized sensitivity (77.61%), the intuitionistic fuzzy configurations achieved the highest specificity (89.80%), indicating a conservative uncertainty-aware classification behavior. The proposed distance measure yielded classification performance highly comparable to that of the conventional Hamming distance, providing an alternative maximum-deviation-based similarity formulation within the intuitionistic fuzzy framework. Furthermore, combining class balancing with feature weighting contributed to a more balanced sensitivity–specificity profile. Overall, the findings demonstrate how uncertainty representation, feature weighting, class balancing, and neighborhood configuration influence classification outcomes, highlighting the potential of intuitionistic fuzzy approaches for uncertainty-aware medical decision-support systems.