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We sought to address this by using a novel radiofrequency (RF) glucose sensor to noninvasively classify glycemic status. The study included 31 participants aged 18\u201365 with prediabetes or type 2 diabetes and no other significant medical history. During control sessions and oral glucose tolerance test sessions, data were collected from both a RF sensor that rapidly scans thousands of frequencies and concurrently from a venous blood draw measured with an US Food and Drug Administration (FDA)-cleared glucose hospital meter system to create paired observations. We trained a time series forest machine learning model on 80% of the paired observations and reported results from applying the model to the remaining 20%. 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