An artificial neural network model for the prediction of child physical abuse recurrences

All 50 states have passed some form of mandatory reporting law to qualify for funding under the Child Abuse Prevention and Treatment act of 1974 (P.L. 93-247). Consequently, child protective service (CPS) agencies have experienced a dramatic increase in reports of abuse and neglect without corresponding increases in funding over the past several years. In response, many CPS agencies have turned to formal risk assessment systems to aid caseworker in making various decisions. Various methodological obstacles have impeded efforts to predict child abuse.

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=5612) were examined. Thirteen predictor variables and five interaction terms were identified for analysis.

It was concluded that both BLR and ANNs offer powerful tools to be used in future efforts to build abuse prediction models. When applied to the present data, BLR was more useful.