  Cardiovascular Disease (CVD) is an important cause of disability and death
among individuals with Diabetes Mellitus (DM). International clinical
guidelines for the management of Type 2 DM (T2DM) are founded on primary and
secondary prevention and favor the evaluation of CVD related risk factors
towards appropriate treatment initiation. CVD risk prediction models can
provide valuable tools for optimizing the frequency of medical visits and
performing timely preventive and therapeutic interventions against CVD events.
The integration of explainability modalities in these models can enhance human
understanding on the reasoning process, maximize transparency and embellish
trust towards the models' adoption in clinical practice. The aim of the present
study is to develop and evaluate an explainable personalized risk prediction
model for the fatal or non-fatal CVD incidence in T2DM individuals. An
explainable approach based on the eXtreme Gradient Boosting (XGBoost) and the
Tree SHAP (SHapley Additive exPlanations) method is deployed for the
calculation of the 5-year CVD risk and the generation of individual
explanations on the model's decisions. Data from the 5-year follow up of 560
patients with T2DM are used for development and evaluation purposes. The
obtained results (AUC = 71.13%) indicate the potential of the proposed approach
to handle the unbalanced nature of the used dataset, while providing clinically
meaningful insights about the ensemble model's decision process.
