NeuroEvolve: A brain-inspired mutation optimization algorithm for enhancing intelligence in medical data analysis

Medical data analysis often suffers from high dimensionality, noise, and complex non-linear patterns, causing traditional optimization-based learning methods to converge slowly and leads to suboptimal predictions. To address these challenges, we propose NeuroEvolve, a brain-inspired mutation strategy integrated into Differential Evolution (DE) that dynamically adjusts mutation factors based on feedback, enhancing both exploration and exploitation. The method was evaluated on three benchmark medical datasets—MIMIC-III, Diabetes, and Lung Cancer—using standard performance metrics including Accuracy, F1-score, Precision, Recall, and a newly defined Mean Error Correlation Coefficient (MECC). Comparative experiments with state-of-the-art evolutionary optimizers demonstrate NeuroEvolve’s superiority. For instance, on the MIMIC-III dataset, NeuroEvolve achieved an Accuracy of 94.1% and an F1-score of 91.3%, reflecting an improvement of 4.5% in Accuracy and 6.2% in F1-score over the best-performing baseline Hybrid Whale Optimization Algorithm (HyWOA). Similar improvements were consistently observed across the Diabetes and Lung Cancer datasets, confirming the robustness of the our approach. These results highlight the potential of NeuroEvolve as an effective optimization-driven learning strategy for complex medical prediction tasks. • A novel mutation-based optimization algorithm, inspired by the brain’s adaptability, integrates Evolutionary Computing and Neurobiology for advanced medical data processing. • Achieves exceptional results in disease detection, therapy optimization, and outcome prediction, outperforming existing methods like HyGWO and HyWOA on benchmark datasets. • High accuracy (up to 95%) and robustness across MIMIC-III, Diabetes, and Lung Cancer datasets, advancing healthcare decision-making and innovation.