An Artificial Neural Network Model for the Prediction of Child Physical Abuse Recurrences

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=5612) were examined. Thirteen predictor variables and five interaction terms were identified for analysis. It was hypothesized that each of the thirteen predictor variables and five interaction terms would be correlated with abuse recurrence when controlling for all other variables in the model. Using binary logistic regression (BLR) to analyze data, only four of the main effect variables and one interaction term were correlated with abuse recurrence.

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Document Details

Document Type
Technical Report
Publication Date
Jul 31, 2001
Accession Number
ADA393607

Entities

People

  • Christopher W. Flaherty

Organizations

  • University of Tennessee system

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Bayesian Networks
  • Biomedical Research
  • Computer Programs
  • Crime
  • Data Mining
  • Ethnic Groups
  • Families (Human)
  • Health Services
  • Information Science
  • Lung Diseases
  • Machine Learning
  • Medical Personnel
  • Neural Networks
  • Psychology

Readers

  • Child and Adolescent Substance Abuse Science in Autism Spectrum Disorders.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Microelectronics