Core Concepts
Machine learning models can effectively identify individuals at risk for Type 2 Diabetes and Prediabetes in Argentina.
Stats
"The database was thoroughly preprocessed and three specific datasets were generated considering a compromise between the number of records and the amount of available variables."
"RF, DT, and ANN demonstrated great classification power, with good values for the metrics under consideration."
Quotes
"Detecting Type 2 Diabetes (T2D) and Prediabetes (PD) is a real challenge for medicine due to the absence of pathogenic symptoms and the lack of known associated risk factors."
"Given the lack of this type of tool in Argentina, this work represents the first step towards the development of more sophisticated models."