Comparison of logistic regression and neural network analysis applied to predicting living setting after hip fracture

Kenneth J. Ottenbacher, Richard T. Linn, Pamela M. Smith, Sandra B. Illig, Melodee Mancuso, Carl V. Granger

    Research output: Contribution to journalArticlepeer-review

    38 Scopus citations


    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.

    Original languageEnglish (US)
    Pages (from-to)551-559
    Number of pages9
    JournalAnnals of Epidemiology
    Issue number8
    StatePublished - Sep 2004


    • Joint Replacement
    • Rehabilitation Outcomes

    ASJC Scopus subject areas

    • Epidemiology


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