A case-based ensemble learning system for explainable breast cancer recurrence prediction

作者：

Highlights：

• This paper proposes a medical diagnosis system combining ensemble learning and CBR.

• Similar cases provide an interpretation of the predictions of ensemble classifiers.

• The interpretability improves doctors’ trust in and acceptance of the system.

• The system is evaluated in a case study about breast cancer recurrence prediction.

摘要

•This paper proposes a medical diagnosis system combining ensemble learning and CBR.•Similar cases provide an interpretation of the predictions of ensemble classifiers.•The interpretability improves doctors’ trust in and acceptance of the system.•The system is evaluated in a case study about breast cancer recurrence prediction.

论文关键词：Ensemble learning,Case-based reasoning,Breast cancer,Recurrence prediction,Case-based interpretation

论文评审过程：Received 28 August 2019, Revised 2 April 2020, Accepted 3 April 2020, Available online 5 June 2020, Version of Record 8 June 2020.

论文官网地址：https://doi.org/10.1016/j.artmed.2020.101858