Literature DB >> 15350954

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, Sandra B Illig, Melodee Mancuso, Carl V Granger.   

Abstract

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.

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Mesh:

Year:  2004        PMID: 15350954     DOI: 10.1016/j.annepidem.2003.10.005

Source DB:  PubMed          Journal:  Ann Epidemiol        ISSN: 1047-2797            Impact factor:   3.797


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