DVTPRED: an explainable machine learning framework for the early prediction of deep vein thrombosis

Deep Vein Thrombosis (DVT) is a serious health issue in which a thrombus (blood clot) forms in one of the veins (typically in the legs). This condition can lead to various severe complications, such as post-thrombotic syndrome, pulmonary embolism, chronic vein insufficiency, and vein gangrene. It is challenging to diagnose DVT in advance since it can also occur without causing any symptoms. Hence, this study uses explainable machine learning techniques to predict DVT. Explainable Artificial Intelligence (XAI) techniques make the algorithms more robust due to their interpretability and transparency. Five different explainers have been utilized in this research, and according to them, ankle swelling, leg swelling, immobilization, and edema are the most essential parameters to identify DVT in patients. The explainable models can be used to predict DVT in advance so that appropriate care and medical attention can be provided to prevent the severe complications induced by this deadly condition.