Predicting Child Physical Abuse Recurrence: Comparison of a Neural Network to Logistic Regression

ABSTRACT

The present study explored the potential of an artificial neural network to improve prediction of recurrences of child physical abuse. Conducted on electronic data file compiled by the U.S. Air Force's central registry of child abuse reports, selected variables pertaining to all child physical abuse reports received from 1990–2000 (N = 5,612) were examined. Thirteen predictor variables and five interaction terms were identified for analysis. The neural network ultimately did not outperform an alternative method, binary logistic regression.