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This study introduces a novel neuro-fuzzy approach with multimodal feature fusion for analysing ECG big data. It addresses challenges of nonlinear and large-scale data, highlighting limitations of traditional and deep learning methods. The proposed methodology uses ECG-to-image conversion, transformer-based feature extraction, and unified neuro-fuzzy fusion, improving interpretability and accuracy. Experimental evaluation on MIT-BIH datasets shows superior performance with 98.46% accuracy and...