  Dry eye disease is a common disorder of the ocular surface, leading patients
to seek eye care. Clinical signs and symptoms are currently used to diagnose
dry eye disease. Metabolomics, a method for analyzing biological systems, has
been found helpful in identifying distinct metabolites in patients and in
detecting metabolic profiles that may indicate dry eye disease at early stages.
In this study, we explored using machine learning and metabolomics information
to identify which cataract patients suffered from dry eye disease. As there is
no one-size-fits-all machine learning model for metabolomics data, choosing the
most suitable model can significantly affect the quality of predictions and
subsequent metabolomics analyses. To address this challenge, we conducted a
comparative analysis of nine machine learning models on three metabolomics data
sets from cataract patients with and without dry eye disease. The models were
evaluated and optimized using nested k-fold cross-validation. To assess the
performance of these models, we selected a set of suitable evaluation metrics
tailored to the data set's challenges. The logistic regression model overall
performed the best, achieving the highest area under the curve score of 0.8378,
balanced accuracy of 0.735, Matthew's correlation coefficient of 0.5147, an
F1-score of 0.8513, and a specificity of 0.5667. Additionally, following the
logistic regression, the XGBoost and Random Forest models also demonstrated
good performance.
