@InProceedings{Hernandez2024InterpretingML,
    author = {Hern{\'a}ndez-P{\'e}rez, Carlos and Pach{\'o}n-Garc{\'i}a, Cristian and Delicado, Pedro and Vilaplana, Ver{\'o}nica},
    editor = {Juarez, Jose M. and Fernandez-Llatas, Carlos and Bielza, Concha and Johnson, Owen and Kocbek, Primoz and Larra{\~{n}}aga, Pedro and Martin, Niels and Munoz-Gama, Jorge and {\v{S}}tiglic, Gregor and Sepulveda, Marcos and Vellido, Alfredo},
    title = {Interpreting Machine Learning Models for Survival Analysis: A Study of Cutaneous Melanoma Using the SEER Database},
    booktitle = {Explainable Artificial Intelligence and Process Mining Applications for Healthcare},
    year = {2024},
    publisher = {Springer Nature Switzerland},
    address = {Cham},
    pages = {52--61},
    abstract = {In this study, we train and compare three types of machine learning algorithms for Survival Analysis: Random Survival Forest, DeepSurv and DeepHit, using the SEER database to model cutaneous malignant melanoma. Additionally, we employ SurvLIMEpy library, a Python package designed to provide explainability for survival machine learning models, to analyse feature importance. The results demonstrate that machine learning algorithms outperform the Cox Proportional Hazards Model. Our work underscores the importance of explainability methods for interpreting black-box models and provides insights into important features related to melanoma prognosis.},
    isbn = {978-3-031-54303-6}
}