A Deep Fuzzy Inference System for Interpretable Multi‐Class Heart Disease Risk Prediction

ABSTRACT Heart disease remains a leading global health concern, necessitating accurate and interpretable risk prediction models for effective clinical decision‐making. Accurate heart disease risk prediction is crucial for preventive healthcare, yet traditional machine learning models often struggle with the inherent uncertainty and nonlinear patterns in medical data. While deep neural networks (DNNs) excel at feature extraction, their lack of interpretability limits clinical utility, whereas fuzzy inference systems (FIS) offer transparency but lack hierarchical learning capabilities. To bridge this gap, we propose a novel Deep Fuzzy Inference System (DFIS) that integrates DNNs and FIS into a unified architecture, combining the strengths of both approaches. The DFIS leverages a weighted fusion mechanism to combine probabilistic outputs from an Adam Cuckoo Search‐optimized DNN and a trapezoidal membership‐based FIS, enabling simultaneous high accuracy and interpretability. The performance is evaluated on the Cleveland heart disease dataset; the DFIS achieves 97.2% accuracy, outperforming a standalone DNN (95.4%) and ANFIS (91.7%) under the same experimental conditions while providing clinically actionable risk stratification into normal, less critical, and very critical categories.