  One component of precision medicine is to construct prediction models with
their predictive ability as high as possible, e.g. to enable individual risk
prediction. In genetic epidemiology, complex diseases have a polygenic basis
and a common assumption is that biological and genetic features affect the
outcome under consideration via interactions. In the case of omics data, the
use of standard approaches such as generalized linear models may be suboptimal
and machine learning methods are appealing to make individual predictions.
However, most of these algorithms focus mostly on main or marginal effects of
the single features in a dataset. On the other hand, the detection of
interacting features is an active area of research in the realm of genetic
epidemiology. One big class of algorithms to detect interacting features is
based on the multifactor dimensionality reduction (MDR). Here, we extend the
model-based MDR (MB-MDR), a powerful extension of the original MDR algorithm,
to enable interaction empowered individual prediction. Using a comprehensive
simulation study we show that our new algorithm can use information hidden in
interactions more efficiently than two other state-of-the-art algorithms,
namely the Random Forest and Elastic Net, and clearly outperforms these if
interactions are present. The performance of these algorithms is comparable if
no interactions are present. Further, we show that our new algorithm is
applicable to real data by comparing the performance of the three algorithms on
a dataset of rheumatoid arthritis cases and healthy controls. As our new
algorithm is not only applicable to biological/genetic data but to all datasets
with discrete features, it may have practical implications in other
applications as well, and we made our method available as an R package.
