Diabetes
This dataset contains parameter values of the model presented by the manuscript titled "Model-Based Extraction of T2D Diagnostic Information from Continuous Glucose Monitoring". The parameters are obtained by first extracting hyperglycemic excursions, i.e. peaks, from continuous glucose data. A simple mathematical model describing glucose homeostasis is fitted to each peak, resulting in a collection of model parameters for each peak.
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Gestational diabetes is a type of high blood sugar that develops during pregnancy. It can occur at any stage of pregnancy and cause problems for both the mother and the baby, during and after birth. The risks can be reduced if they are early detected and managed, especially in areas where only periodic tests of pregnant women are available. Intelligent systems designed by machine learning algorithms are remodelling all fields of our lives, including the healthcare system. This study proposes a combined prediction model to diagnose gestational diabetes.
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The Cross-sectional Diabetes Risk survey aims to assess the prevalence of diabetes and its risk factors at the same point in time and also provide a "snapshot" of diseases and risk factors simultaneously for individuals belonging to the western region of the Kingdom of Saudi Arabia (KSA).
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