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Cardiovascular Disease (CVD) is an important cause of disability and deathamong individuals with Diabetes Mellitus (DM). International clinicalguidelines for the management of Type 2 DM (T2DM) are founded on primary andsecondary prevention and favor the evaluation of CVD related risk factorstowards appropriate treatment initiation. CVD risk prediction models canprovide valuable tools for optimizing the frequency of medical visits andperforming timely preventive and therapeutic interventions against CVD events.The integration of explainability modalities in these models can enhance humanunderstanding on the reasoning process, maximize transparency and embellishtrust towards the models' adoption in clinical practice. The aim of the presentstudy is to develop and evaluate an explainable personalized risk predictionmodel for the fatal or non-fatal CVD incidence in T2DM individuals. Anexplainable approach based on the eXtreme Gradient Boosting (XGBoost) and theTree SHAP (SHapley Additive exPlanations) method is deployed for thecalculation of the 5-year CVD risk and the generation of individualexplanations on the model's decisions. Data from the 5-year follow up of 560patients with T2DM are used for development and evaluation purposes. Theobtained results (AUC = 71.13%) indicate the potential of the proposed approachto handle the unbalanced nature of the used dataset, while providing clinicallymeaningful insights about the ensemble model's decision process.

An explainable XGBoost-based approach towards assessing the risk of cardiovascular disease in patients with Type 2 Diabetes Mellitus