Abstract:UNSTRUCTURED
Identifying and delivering interventions to patients with prediabetes was one strategy for dealing with the rising prevalence of T2DM. Risk assessment tools help in disease detection by allowing screening of the high risk group. Machine learning was also used to support in the detection and diagnosis of prediabetes. The purpose of this review is to assess the diagnostic test accuracy of various machine learning algorithms for calculating prediabetes risk. This protocol was … Show more
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