Measuring the effects of confounders in medical supervised classification problems: the Confounding Index (CI)
作者：
Highlights：
• A novel index is introduced to measure the confounding effect of a categorical variable in classification studies.
• The index is also used for continuously distributed variables, by binning their values.
• The index can rank the effect of various variables, allowing to determine affordable matching criteria.
• The index can assess the effectiveness of a normalization procedure and the robustness of a learning model against confounding effects.
• The validity and usefulness of the index is proved both on simulated and real-world neuroimaging data.
摘要
•A novel index is introduced to measure the confounding effect of a categorical variable in classification studies.•The index is also used for continuously distributed variables, by binning their values.•The index can rank the effect of various variables, allowing to determine affordable matching criteria.•The index can assess the effectiveness of a normalization procedure and the robustness of a learning model against confounding effects.•The validity and usefulness of the index is proved both on simulated and real-world neuroimaging data.
论文关键词：Machine learning,Confounding variables,Biomedical data,Classification
论文评审过程：Received 10 May 2019, Revised 8 November 2019, Accepted 10 January 2020, Available online 13 January 2020, Version of Record 2 February 2020.
论文官网地址：https://doi.org/10.1016/j.artmed.2020.101804
