Enhancing diabetes prediction performance using feature selection based on grey wolf optimizer with autophagy mechanism

• The proposed method obtains a notable accuracy of 90.91% and outperforms existing methods. • Improving feature selection accuracy for better predictive model outcomes. • The novelty of this research lies in combining Grey Wolf Optimizer with Autophagy Mechanism to create a robust feature selection for the prediction of diabetes disease. • The proposed method is tested on additional datasets, including the glioma and lung cancer, showcasing its broad applicability and robustness. Diabetes mellitus, often called a silent killer, is a chronic condition characterized by insufficient insulin production and elevated blood sugar levels, leading to complications in vital organs such as the nerves, eyes, and kidneys. Machine learning is a powerful tool for predicting diabetes; however, noisy features can negatively impact its accuracy, making an effective feature selection essential. This study proposes an improved feature selection approach for diabetes prediction, leveraging the Grey Wolf Optimizer with an integrated Autophagy Mechanism (GWO-AM) on the Pima Indian Diabetes Dataset. The autophagy mechanism, inspired by cellular self-degradation and recycling, is incorporated into GWO to enhance exploration and exploitation. The method was also tested on glioma and lung cancer datasets to assess scalability. Comprehensive experiments demonstrate that GWO-AM significantly improves prediction accuracy while reducing the number of selected features. For the diabetes dataset, GWO-AM achieved an accuracy of 90.91%, outperforming existing methods. It also excelled in the glioma and lung cancer datasets, highlighting its potential for application to other medical datasets.