A major random 1,3-satisfiability logic mining model in discrete Hopfield neural networks for adverse event analysis

Logic mining in neural networks enables the extracting of interpretable symbolic knowledge from complex datasets. This study proposes a logic mining approach in discrete Hopfield neural networks based on a non-systematic major random 1,3-satisfiability (MR1,3SAT) logical structure for adverse event analysis in Alzheimer's disease datasets. The proposed model incorporates first- and third-order logical clauses to enhance representational flexibility while maintaining computational tractability. A feature selection mechanism based on Jaccard analysis is incorporated to identify relevant attributes, while the intelligent artificial bee colony algorithm is applied during the retrieval phase to refine the final neuron states. Experimental evaluation on Alzheimer's disease neuroimaging initiative datasets demonstrate that the proposed model achieves superior performance compared with existing logic mining approaches, obtaining 90.23% accuracy, 98% sensitivity, 99% specificity, and a Fowlkes-Mallows index of 93.46%. These results indicate that the MR1,3SAT-based logic mining model improves both interpretability and classification reliability for adverse event symptoms analysis in Alzheimer's disease neuroimaging initiative datasets.