Characterization of Type 2 Diabetes Using Counterfactuals and Explainable AI

Lenatti M., Carlevaro A., Keshavjee K., Guergachi A., Paglialonga A., Mongelli M.

32nd Medical Informatics Europe Conference, MIE 2022, Nice, France, 27 - 30 May 2022, vol.294, pp.98-103 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 294
  • Doi Number: 10.3233/shti220404
  • City: Nice
  • Country: France
  • Page Numbers: pp.98-103
  • Keywords: Counterfactual Explanations, Diabetes, eXplainable AI
  • TED University Affiliated: No


© 2022 European Federation for Medical Informatics (EFMI) and IOS Press.Type 2 diabetes mellitus is a metabolic disorder of glucose management, whose prevalence is increasing inexorably worldwide. Adherence to therapies, along with a healthy lifestyle can help prevent the onset of disease. This preliminary study proposes the use of explainable artificial intelligence techniques with the aim of (i) characterizing diabetic patients through a set of easily interpretable rules and (ii) providing individualized recommendations for the prevention of the onset of the disease through the generation of counterfactual explanations, based on minimal variations of biomarkers routinely collected in primary care. The results of this preliminary study parallel findings from the literature as differences in biomarkers between patients with and without diabetes are observed for fasting blood sugar, body mass index, and high-density lipoprotein levels.