DiaClusT Explorer

Version 1

As part of the DiaClusT project, we developed a multinomial logistic regression classifier to assign patients to distinct type 2 diabetes clusters based on their clinical characteristics. The current approach, including age, sex, BMI, HbA1c, and triglyceride levels, achieved an overall accuracy of 92%, with sensitivity, specificity, and F1-score all exceeding 0.87. The model’s robust performance highlights the potential of incorporating cluster assignment into routine admission workflows, facilitating the early identification of patients at elevated risk for comorbidities and adverse inpatient outcomes.