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Comparison of logistic regression and neural network analysis applied to predicting living setting after hip fracture
Kenneth J Ottenbacher1, Richard T Linn, Pamela M Smith
1Division of Rehabilitation Sciences, University of Texas Medical Branch, Galveston, TX 77555-1137, USA. kottenba@utmb.edu
Purpose:
Describe and compare the characteristics of artificial neural networks and logistic regression to develop prediction models in epidemiological research.
Methods:
The sample included 3708 persons with hip fracture from 46 different states included in the Uniform Data System for Medical Rehabilitation. Mean age was 75.5 years (sd=14.2), 73.7% of patients were female, and 82% were non-Hispanic white. Average length of stay was 17.0 days (sd=10.6). The primary outcome measure was living setting (at home vs. not at home) at 80 to 180 days after discharge.
Results:
Statistically significant variables (p <.05) in the logistic model included follow-up therapy, sphincter control, self-care ability, marital status, age, and length of stay. Areas under the receiver operating characteristic curves were 0.67 for logistic regression and 0.73 for neural network analysis. Calibration curves indicated a slightly better fit for the neural network model.
Conclusions:
Follow-up therapy and independent bowel and/or bladder function were strong predictors of living at home up to 6 months after hospitalization for hip fracture. No practical differences were found between the predictive ability of logistic regression and neural network analysis in this sample.