ArticleBritish journal of medicine and medical research
Behavior Correlates of Post-Stroke Disability Using Data Mining and Infographics.
Article in British journal of medicine and medical research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeDisability 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.
methodsData 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.
resultsFifty-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).
conclusionsData 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.
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