Machine learning-based reproducible prediction of type 2 diabetes subtypes.
Hayato TanabeMasahiro SatoAkimitsu MiyakeYoshinori ShimajiriTakafumi OjimaAkira NaritaHaruka SaitoKenichi TanakaHiroaki MasuzakiJunichiro J KazamaHideki KatagiriGen TamiyaEiryo KawakamiMichio ShimabukuroPublished in: Diabetologia (2024)
The new ML model for predicting Ahlqvist's subtypes of type 2 diabetes has great potential for application in clinical practice and cohort studies because it can classify individuals with missing HOMA2 indices and predict glycaemic control, diabetic complications and treatment outcomes with long-term consistency by using readily available variables. Future studies are needed to assess whether our approach is applicable to research and/or clinical practice in multiethnic populations.