ANALYSIS OF DIABETES MELLITUS DIAGNOSIS USING ARTIFICIAL INTELLIGENCE IN PRIMARY HEALTHCARE

Abstract

Diabetes mellitus is one of the most widespread chronic diseases worldwide and requires early detection to prevent serious complications. However, diagnosing diabetes in primary healthcare settings often faces challenges due to the high number of patients, limited diagnostic resources, and variability in clinical decision-making. The integration of artificial intelligence technologies into medical practice offers new opportunities to improve the efficiency and accuracy of diagnostic processes. This study focuses on the analysis of diabetes diagnosis using artificial intelligence methods in primary healthcare institutions. Machine learning algorithms and data analysis techniques are applied to evaluate patient clinical indicators, laboratory results, and medical histories associated with diabetes risk. The use of intelligent diagnostic systems allows for the rapid processing of large medical datasets and the identification of hidden patterns that may indicate the early development of the disease.