TY - CONF
TI - An explainable XGBoost-based approach towards assessing the risk of
cardiovascular disease in patients with Type 2 Diabetes Mellitus
AU - Athanasiou, Maria
AU - Sfrintzeri, Konstantina
AU - Zarkogianni,
AU - Konstantia
AU - Thanopoulou, Anastasia C.
AU - Nikita, Konstantina S.
PY - 2020
SP - 859-864
PB - IEEE Comput. Soc
T2 - 2020 IEEE 20TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND
BIOENGINEERING (BIBE 2020)
TODO - 10.1109/BIBE50027.2020.00146
TODO - Cardiovascular Disease; Diabetes; machine learning; explainability;
interpretability; unbalanced data
TODO - 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 model’s decision process.
ER -