A Metamodel Structure For Regression Analysis: Application To Prediction Of Autism Spectrum Disorder Severity
This work addresses the challenge of improving regression accuracy for small, noisy datasets in the domain of medical diagnosis, specifically for predicting autism severity, though it appears incremental as it builds on existing classification and regression methods.
The authors tackled the problem of poor generalization in regression with small, noisy datasets by proposing a novel metamodel structure, which outperformed traditional regression models in predicting autism spectrum disorder severity from fMRI data, achieving higher Pearson correlation coefficients and stability.
Traditional regression models do not generalize well when learning from small and noisy datasets. Here we propose a novel metamodel structure to improve the regression result. The metamodel is composed of multiple classification base models and a regression model built upon the base models. We test this structure on the prediction of autism spectrum disorder (ASD) severity as measured by the ADOS communication (ADOS COMM) score from resting-state fMRI data, using a variety of base models. The metamodel outperforms traditional regression models as measured by the Pearson correlation coefficient between true and predicted scores and stability. In addition, we found that the metamodel is more flexible and more generalizable.