Machine learning models selection under uncertainty: application in cancer prediction
Cancer stands as the foremost global cause of mortality, with millions of new cases diagnosed each year. Many research papers have discussed the potential benefits of Machine Learning (ML) in cancer prediction, including improved early detection and personalized treatment options. The literature also highlights the challenges facing the field, such as the need for large and diverse datasets as well as interpretable models with high performance. The aim of this paper is to suggest a new approach in order to select and assess the generalization performance of ML models