Relationship between Event Prevalence Rate and Gini Coefficient of Predictive Model

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https://ccsenet.org/journal/index.php/jmr/article/view/0/46616

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https://doi.org/10.5539/jmr.v14n1p46
 http://hdl.handle.net/11603/31690

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UMBC Information Systems Department
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UMBC Computer Science and Electrical Engineering Department
UMBC Erickson School of Aging Studies
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https://orcid.org/0000-0003-2454-4187
 https://orcid.org/0000-0002-3995-339X

Date

2022-02

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journal articles
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Citation of Original Publication

Han, Fei, and Ian Stockwell. “Relationship between Event Prevalence Rate and Gini Coefficient of Predictive Model.” Journal of Mathematics Research 14, no. 1 (January 14, 2022):46-50. https://doi.org/10.5539/jmr.v14n1p46.

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Abstract

Predictive models are currently used for early intervention to help identify patients with a high risk of adverse events. Assessing the accuracy of such models is a crucial part of the development process. To measure the predictive performance of a scoring model, quantitative indices such as the K-S statistic and C-statistic are used. This paper discusses the relationship between Gini coefficients and event prevalence rates. The main contribution of the paper is the theoretical proof of the relationship between the Gini coefficient and event prevalence rate.