Designing an Intelligent System for Diagnosing Diabetes with the Help of the XCSLA System

An intelligent method for diagnosing diabetes is introduced in this article. One of the main problems involved in this disease is that it is not diagnosed correctly and in time and, due to the destructive effects of the progression of the disease on the human body, the need for its timely prediction and diagnosis is felt more than ever before. At present, doctors diagnose diabetes based on documents, scientific tests, and their own experience. However, considering the huge number of patients, a decision support system for recognizing the disease pattern in diabetics can be used. Results of Program Implementation Document (PID) on databases indicated the higher efficiency of the proposed method in diagnosing diabetes compared to the classic XCS system, the ELMAN neural network, SVM clustering, KNN, C4.5, and AD Tree.