Selecting optimal cut-off points for multiple variables in binary outcome settings

Runhua Zhou; Runhan Liu

doi:10.1117/12.2656755

13 January 2023 Selecting optimal cut-off points for multiple variables in binary outcome settings

Runhua Zhou, Runhan Liu

Proceedings Volume 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022); 1251011 (2023) https://doi.org/10.1117/12.2656755
Event: International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 2022, Qingdao, China

Abstract

Categorization is an important and common practice in medical research, and finding cut-off points is a direct way of classification. The existing methods are mainly about computing cut-off points for only one variable. This paper proposes an algorithm to select cut-off points for multiple variables in binary outcome settings. This algorithm extends the search for cut-off points of one variable to multiple variables and is applied to the Breast Cancer Coimbra dataset. It shows a good classification effect and gives the confidence intervals of the cut-off points of the prognostic variables.

Citation Download Citation

Runhua Zhou and Runhan Liu "Selecting optimal cut-off points for multiple variables in binary outcome settings", Proc. SPIE 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 1251011 (13 January 2023); https://doi.org/10.1117/12.2656755

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