Provide Diagnosis of Diabetes Based on Intelligent Feature Reduction and Machine Learning

Extraction and analysis from a large number of data related to disease records and medical records of individuals using the data mining process can lead to the identification of the rules governing the accurate diagnosis of diseases and provide valuable information for accuracy in the disease, forecast and Diagnosis of the disease to be provided to health professionals according to the prevailing environmental factors. The aim of this study was to diagnose diabetes using a combination of linear separator analysis and gray wolf algorithm based on PIDD database and Python language. We were able to provide higher accuracy by using this combination and by reducing the feature, so we achieved a 6% improvement.