A Longitudinal Support Vector Regression for Prediction of ALS Score

Authors

• W Du
• H Cheung
• CA Johnson
• I Goldberg
• M Thambisetty
• K Becker

Abstract

Longitudinal studies play a key role in various fields, including epidemiology, clinical research, and genomic analysis. Currently, the most popular methods in longitudinal data analysis are model-driven regression approaches, which impose strong prior assumptions and are unable to scale to large problems in the manner of machine learning algorithms. In this work, we propose a novel longitudinal support vector regression (LSVR) algorithm that not only takes the advantage of one of the most popular machine learning methods, but also is able to model the temporal nature of longitudinal data by taking into account observational dependence within subjects. We test LSVR on a publicly available Amyotrophic Lateral Sclerosis (ALS) data-set from the DREAM-Phil Bowen ALS Prediction Prize4Life challenge. Results suggest that LSVR is at a minimum competitive with favored machine learning methods and is able to outperform those methods in predicting ALS score one month in advance.

Scientific Focus Area: Computational Biology

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