  Literature on machine learning for multiple sclerosis has primarily focused
on the use of neuroimaging data such as magnetic resonance imaging and clinical
laboratory tests for disease identification. However, studies have shown that
these modalities are not consistent with disease activity such as symptoms or
disease progression. Furthermore, the cost of collecting data from these
modalities is high, leading to scarce evaluations. In this work, we used
multi-dimensional, affordable, physical and smartphone-based performance
outcome measures (POM) in conjunction with demographic data to predict multiple
sclerosis disease progression. We performed a rigorous benchmarking exercise on
two datasets and present results across 13 clinically actionable prediction
endpoints and 6 machine learning models. To the best of our knowledge, our
results are the first to show that it is possible to predict disease
progression using POMs and demographic data in the context of both clinical
trials and smartphone-base studies by using two datasets. Moreover, we
investigate our models to understand the impact of different POMs and
demographics on model performance through feature ablation studies. We also
show that model performance is similar across different demographic subgroups
(based on age and sex). To enable this work, we developed an end-to-end
reusable pre-processing and machine learning framework which allows quicker
experimentation over disparate MS datasets.
