AN EXPLAINABLE HYBRID AI FRAMEWORK USING FUZZY ROUGH SET RULES FOR MENTAL HEALTH PREDICTION - Rustam, Rustam (Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang, 2025)
The increasing use of artificial intelligence (AI) in mental health prediction highlights the need for models that are not only accurate but also mathematically interpretable and theoretically grounded. This paper presents a mathematical modeling framework for explainable AI that integrates the K-Nearest Neighbors (KNN) algorithm with rule induction based on fuzzy rough set theory. The proposed hybrid framework is formulated to combine statistical classification with symbolic reasoning, providing transparent post hoc explanations through a set of fuzzy linguistic rules. A large-scale mental health dataset is utilized, comprising behavioral, psychological, and lifestyle attributes, with