Abstract
Extensive dataset availability for neurological disease, such as multiple sclerosis (MS), has led to new methods of risk assessment and disease course prediction, such as using machine learning and other statistical methods. However, many of these methods cannot properly capture complex relationships between variables that affect results of odds ratios unless independence between risk factors is assumed. This work addresses this limitation using a Bayesian network (BN) approach to MS risk assessment that incorporates data from UK Biobank with a counterfactual model, which includes causal knowledge of dependencies between variables. We present the results of more traditional Bayesian measurements such as necessity and sufficiency, along with odds ratios for each of the risk factors in the model. The greatest risk is produced by the genetic factor DRB15 (2.7 OR) but smoking, vitamin D levels, and childhood obesity may also play a role in MS development. Further data collection, especially in infectious mononucleosis in the population, is needed to provide a more accurate measure of risk.
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Hartmann, M., Fenton, N., Dobson, R. (2022). Development of Bayesian Network for Multiple Sclerosis Risk Factor Interaction Analysis. In: Chicco, D., et al. Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2021. Lecture Notes in Computer Science(), vol 13483. Springer, Cham. https://doi.org/10.1007/978-3-031-20837-9_2
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