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Abstract
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By utilizing Artificial intelligence, knowledge can be derived from a vast amount of disease and medical records. This information can lead to the discovery of rules governing disease creation, growth, and acceleration, and provide valuable insights for identifying the causes of disease occurrence. Furthermore, it helps in the diagnosis, prediction, and treatment of diseases based on the prevailing environmental factors available to healthcare professionals. The objective of this research is to diagnose diabetes by employing a combination of LDA and the gray wolf algorithm. This approach was applied to the PIDD dataset. The superiority of the gray wolf algorithm is demonstrated by the measurement of recognition accuracy, which shows a notable improvement
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