Abstract
We report for the first time the integration of ultra-high-pressure liquid chromatography-tandem mass spectrometry with machine learning for identifying fatty acid metabolite biomarkers of ischemic stroke. In particular, we develop an optimal model to discriminate ischemic stroke patients from healthy persons with 100% sensitivity and 93.18% specificity. This research may facilitate understanding the roles of fatty acid metabolites in stroke occurrence, holding great potential in clinical stroke diagnosis.
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