Interpreting Machine Learning Models for Survival Analysis: A Study of Cutaneous Melanoma Using the SEER Database

被引:0
|
作者
Hernandez-Perez, Carlos [1 ]
Pachon-Garcia, Cristian [2 ]
Delicado, Pedro [2 ]
Vilaplana, Veronica [1 ]
机构
[1] Univ Politecn Catalunya Barcelona Tech UPC, Signal Theory & Commun Dept, Barcelona, Spain
[2] Univ Politecn Catalunya Barcelona Tech UPC, Dept Stat & Operat Res, Barcelona, Spain
来源
EXPLAINABLE ARTIFICIAL INTELLIGENCE AND PROCESS MINING APPLICATIONS FOR HEALTHCARE, XAI-HEALTHCARE 2023 & PM4H 2023 | 2024年 / 2020卷
关键词
Survival Analysis; Machine Learning; eXplainable Artificial Intelligence; Melanoma;
D O I
10.1007/978-3-031-54303-6_6
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:52 / 61
页数:10
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