Characterization of Type 2 Diabetes Using Counterfactuals and Explainable AI.

Journal: Studies in health technology and informatics
Published Date:

Abstract

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.

Authors

  • Marta Lenatti
    National Research Council of Italy (CNR), Institute of Electronics, Information Engineering and Telecommunications (IEIIT), Italy.
  • Alberto Carlevaro
    National Research Council of Italy (CNR), Institute of Electronics, Information Engineering and Telecommunications (IEIIT), Italy.
  • Karim Keshavjee
    Research Lab for Advanced System Modelling, Ryerson University, Toronto, ON, M5B 2K3, Canada.
  • Aziz Guergachi
    Research Lab for Advanced System Modelling, Ryerson University, Toronto, ON, M5B 2K3, Canada.
  • Alessia Paglialonga
    Institute of Electronics, Computer and Telecommunication Engineering (IEIIT), National Research Council of Italy (CNR), Milan, Italy.
  • Maurizio Mongelli
    Consiglio Nazionale delle Ricerche (CNR), Institute of Electronics, Information Engineering and Telecommunications (IEIIT), 16149 Genoa, Italy.