Explanations of medical prediction models using background knowledgeLUMBUROVSKA, LEONIDA (Avtor) Robnik Šikonja, Marko (Mentor) artificial intelligencepredictionsbackground knowledgemedicineexplainable AIPrediction models are very useful in many areas, as they provide decisions as well as an understanding of the problem. In medicine, they are often used to predict diseases, outbreaks, reactions to medications, etc. Data scientists are striving to improve these models to get more accurate results as well as a better understanding of different phenomena. Since deep learning models are considered black boxes, the output decisions are not easily explained, but their interpretation would be very beneficial. In this thesis, two different approaches to medical model interpretation are shown. The first explains with contextual decomposition, focusing not only on the importance of singular features but also on interactions between them. This way, we can understand complex features and their role in models. The second approach leverages saliency maps in order to provide visual explanations through parts of images most impactful in the prediction model. A comparison of both methods on a skin cancer dataset shows similarities and differences between the two. The results show that the second approach gives us more understandable explanations, while the first one is more useful when trying to improve models' accuracy.20232023-01-09 13:25:00Diplomsko delo/naloga143700sl