First Experiences with the Identification of People at Risk for Diabetes
  in Argentina using Machine Learning Techniques
Enzo Rucci Gonzalo Tittarelli Franco Ronchetti Jorge F. Elgart Laura Lanzarini Juan José Gagliardino
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. Even though some proposals for machine learning models
enable the identification of people at risk, the nature of the condition makes
it so that a model suitable for one population may not necessarily be suitable
for another. In this article, the development and assessment of predictive
models to identify people at risk for T2D and PD specifically in Argentina are
discussed. First, 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. After applying 5 different
classification models, the results obtained show that a very good performance
was observed for two datasets with some of these models. In particular, RF, DT,
and ANN demonstrated great classification power, with good values for the
metrics under consideration. Given the lack of this type of tool in Argentina,
this work represents the first step towards the development of more
sophisticated models.