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Interpretability methods of machine learning algorithms with applications in breast cancer diagnosis
The best performance for breast cancer diagnosis was achieved by the proposed ENN (96. 6% accuracy and 0. 96 area under the ROC curve), and its predictions were explained by ICE plots, proving that its decisions were compliant with current medical knowledge and can be further utilized to gain new insights in the pathophysiological mechanisms of breast cancer.
Stratification of carotid atheromatous plaque using interpretable deep learning methods on B-mode ultrasound images
Carotid atherosclerosis is the major cause of ischemic stroke resulting in significant rates of mortality and disability annually.
An explainable XGBoost-based approach towards assessing the risk of cardiovascular disease in patients with Type 2 Diabetes Mellitus
Cardiovascular Disease (CVD) is an important cause of disability and death among individuals with Diabetes Mellitus (DM).
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