title: Behavior Correlates of Post-Stroke Disability Using Data Mining and Infographics.
abstract: OBJECTIVE
  Disability is a potential risk for stroke survivors. This study aims to identify disability risk factors associated with stroke and their relative importance and relationships from a national behavioral risk factor dataset.
  
  
  METHODS
  Data of post-stroke individuals in the U.S (n=19,603) including 397 variables were extracted from a publically available national dataset and analyzed. Data mining algorithms including C4.5 and linear regression with M5s methods were applied to build association models for post-stroke disability using Weka software. The relative importance and relationship of 70 variables associated with disability were presented in infographics for clinicians to understand easily.
  
  
  RESULTS
  Fifty-five percent of post-stroke patients experience disability. Exercise, employment and satisfaction of life were relatively important factors associated with disability among stroke patients. Modifiable behavior factors strongly associated with disability include exercise (OR: 0.46, P<0.01) and good rest (OR 0.37, P<0.01).
  
  
  CONCLUSIONS
  Data mining is promising to discover factors associated with post-stroke disability from a large population dataset. The findings can be potentially valuable for establishing the priorities for clinicians and researchers and for stroke patient education. The methods may generalize to other health conditions.
authors.affiliation: School of Nursing, Columbia University, New York, USA.
authors.forename: Sunmoo
authors.initials: S
authors.lastname: Yoon
authors.affiliation: Department of Neurology, Columbia University, New York, USA.
authors.forename: Jose
authors.initials: J
authors.lastname: Gutierrez
keywords: Stroke; data mining; patient outcome; visualization
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