title: Comparison of logistic regression and neural networks to predict rehospitalization in patients with stroke.
abstract: BACKGROUND
  Rehospitalization following inpatient medical rehabilitation has important health and economic implications for patients who have experienced a stroke.
  
  
  OBJECTIVE
  Compare logistic regression and neural networks in predicting rehospitalization at 3-6-month follow-up for patients with stroke discharged from medical rehabilitation.
  
  
  METHODS
  The study was retrospective using information from a national database representative of medical rehabilitation patients across the US.
  
  
  METHODS
  Information submitted to the Uniform Data System for Medical Rehabilitation from 1997 and 1998 by 167 hospital and rehabilitation facilities from 40 states was examined.
  
  
  METHODS
  9584 patient records were included in the sample. The mean age was 70.74 years (SD = 12.87). The sample included 51.6% females and was 77.6% non-Hispanic White with an average length of stay of 21.47 days (SD = 15.47).
  
  
  METHODS
  Hospital readmission from 80 to 180 days following discharge.
  
  
  RESULTS
  Statistically significant variables (P <.05) in the logistic model included sphincter control, self-care ability, age, marital status, ethnicity and length of stay. Area under the ROC curves were 0.68 and 0.74 for logistic regression and neural network analysis, respectively. The Hosmer-Lemeshow goodness-of-fit chi-square was 11.32 (df = 8, P = 0.22) for neural network analysis and 16.33 (df = 8, P = 0.11) for logistic regression. Calibration curves indicated a slightly better fit for the neural network model.
  
  
  CONCLUSIONS
  There was no statistically significant or practical advantage in predicting hospital readmission using neural network analysis in comparison to logistic regression for persons who experienced a stroke and received medical rehabilitation during the period of the study.
authors.affiliation: SAHS 4.202, University of Texas Medical Branch, Galveston, TX 77555-1028, USA. kottenba@utmb.edu
authors.forename: K J
authors.initials: KJ
authors.lastname: Ottenbacher
authors.forename: P M
authors.initials: PM
authors.lastname: Smith
authors.forename: S B
authors.initials: SB
authors.lastname: Illig
authors.forename: R T
authors.initials: RT
authors.lastname: Linn
authors.forename: R C
authors.initials: RC
authors.lastname: Fiedler
authors.forename: C V
authors.initials: CV
authors.lastname: Granger
mesh_terms: D000368:Aged; D005260:Female; D006305:Health Status Indicators; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D010359:Patient Readmission / Q000706:statistics & numerical data*; D011336:Probability; D012372:ROC Curve; D012680:Sensitivity and Specificity; D000071939:Stroke Rehabilitation; D014481:United States / Q000453:epidemiology