Incorporating data heterogeneity for improved regression models: application to stroke

Anuja Sharma, Edward Dibella

Abstract: Symbolic data regression provides a systematic way to bring together heterogenous data from imaging and non-imaging sources in the form of histograms, intervals and scalar-valued observations. Classic multiple linear regression is adapted to mixed symbolic features and applied to data from diffusion spectrum images and clinical measurements for stroke recovery prediction. By utilizing the implicit variability within observations and natural grouping within features, the amount of information available to the modeling process is increased. This provides increased stability for model parameters over traditional regression and is especially beneficial with low sample sizes.