A study released by University at Buffalo was able to use artificial intelligence to establish trends in the United States pertaining to type 2 diabetes.
Published in Scientific Reports, the research group of the study used machine learning, a form of artificial intelligence, to establish the prevalence of type 2 diabetes among the general population.
According to the journal report in Nature: “Our results indicate that a non-parametric GW-RF model shows a high potential for explaining spatial heterogeneity of, and predicting, T2D prevalence over traditional local and global models when inputting six major risk factors. Some of these predictions, however, are marginal.”
The authors continued by stating, “These findings of spatial heterogeneity using GW-RF demonstrate the need to consider local factors in prevention approaches. Spatial analysis of T2D and associated risk factor prevalence offers useful information for targeting the geographic area for prevention and disease interventions.”
The study was authored by Sarah Quinones, Aditya Goyal, and Zia Ahmed.
