The use of machine
learning (ML) with metabolomics provides
opportunities
for the early diagnosis of disease. However, the accuracy of ML and
extent of information obtained from metabolomics can be limited owing
to challenges associated with interpreting disease prediction models
and analyzing many chemical features with abundances that are correlated
and “noisy”. Here, we report an interpretable neural
network (NN) framework to accurately predict disease and identify
significant biomarkers using whole metabolomics data sets withouta priorifeature selection. The performance of the NN approach
for predicting Parkinson’s disease (PD) from blood plasma metabolomics
data is significantly higher than other ML methods with a mean area
under the curve of >0.995. PD-specific markers that predate clinical
PD diagnosis and contribute significantly to early disease prediction
were identified including an exogenous polyfluoroalkyl substance.
It is anticipated that this accurate and interpretable NN-based approach
can improve diagnostic performance for many diseases using metabolomics
and other untargeted ‘omics methods.