Enhancing the Diagnostic Accuracy of Diabetes and Prediabetes with Neural Network-Based Area Under the Curve Analysis of OGTT Data

Diabetes is a chronic disease characterized by persistently elevated blood glucose (BG) levels, which can lead to severe complications such as heart disease, stroke, nephropathy, retinopathy, and neuropathy if left unmanaged. Prediabetes, a precursor to type 2 diabetes, is defined as a state of abnormally high BG levels that fall below the diagnostic threshold for diabetes. The oral glucose tolerance test (OGTT) is a widely used diagnostic tool for identifying individuals with diabetes and prediabetes. This test involves ingesting a glucose solution and measuring BG and insulin levels at specific intervals. Recently, researchers have begun to utilize the area under the curve of insulin (AUCI) and glucose (AUCG) of the OGTT, as diagnostic metrics. These values are calculated by measuring the area beneath the curve formed by the glucose and insulin concentration in the blood throughout the OGTT. Artificial neural networks (ANNs) have shown significant potential in enhancing the diagnosis of diabetes and prediabetes. This study explores the application of ANNs for diagnosing diabetes and prediabetes, utilizing AUCG and AUCI as diagnostic metrics. A data set of 188 individuals diagnosed with diabetes or prediabetes according to World Health Organization criteria was used for the analysis. The results demonstrate high accuracy, exceeding 96.7%, for diabetes prediction using AUCG. The sensitivity, specificity, PPV, and NPV results indicate a low false positive rate and a low false negative rate, especially for predicting diabetes using AUCG. These findings highlight the potential of ANNs, especially when trained on glucose data, for accurate diabetes and prediabetes classification. Future research could explore incorporating additional data and improving performance for non-diabetes predictions.