Novel Strategy for Human Deep Vein Thrombosis Diagnosis Based on Metabolomics and Stacking Machine Learning

Deep vein thrombosis (DVT) is a serious health issue
that often
leads to considerable morbidity and mortality. Diagnosis of DVT in
a clinical setting, however, presents considerable challenges. The
fusion of metabolomics techniques and machine learning methods has
led to high diagnostic and prognostic accuracy for various pathological
conditions. This study explored the synergistic potential of dual-platform
metabolomics (specifically, gas chromatography–mass spectrometry
(GC-MS) and liquid chromatography–mass spectrometry (LC-MS))
to expand the detection of metabolites and improve the precision of
DVT diagnosis. Sixty-one differential metabolites were identified
in serum from DVT patients: 22 from GC-MS and 39 from LC-MS. Among
these, five key metabolites were highlighted by SHapley Additive exPlanations
(SHAP)-guided feature engineering and then used to develop a stacking
diagnostic model. Additionally, a user-friendly interface application
system was developed to streamline and automate the application of
the diagnostic model, enhancing its practicality and accessibility
for clinical use. This work showed that the integration of dual-platform
metabolomics with a stacking machine learning model enables faster
and more accurate diagnosis of DVT in clinical environments.