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Testing the Utility of the Neural Network Model to Predict History of Arrest among Intimate Partner Violent Men

1
Department of Psychology, University of Houston, 4800 Calhoun Rd, Houston, TX 77004, USA
2
Private Practice, Plano, TX 75074, USA
*
Author to whom correspondence should be addressed.
Received: 28 September 2018 / Revised: 18 December 2018 / Accepted: 28 December 2018 / Published: 10 January 2019
(This article belongs to the Special Issue Social Safety and Security)
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PDF [205 KB, uploaded 10 January 2019]

Abstract

Risk assessments are typically based on retrospective reports of factors known to be correlated with violence recidivism in simple linear models. Generally, these linear models use only the perpetrators’ reports. Using a community sample of couples recruited for recent male-to-female intimate partner violence (IPV; N = 97 couples), the current study compared non-linear neural network models to traditional linear models in predicting a history of arrest in men who perpetrate IPV. The neural network models were found to be superior to the linear models in their predictive power. Models were slightly improved by adding victims’ report. These findings suggest that the prediction of violence arrest be enhanced through the use of neural network models and by including collateral reports. View Full-Text
Keywords: intimate partner violence; neural network; violence risk assessment intimate partner violence; neural network; violence risk assessment
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Babcock, J.C.; Cooper, J. Testing the Utility of the Neural Network Model to Predict History of Arrest among Intimate Partner Violent Men. Safety 2019, 5, 2.

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