Rapid noninvasive screening of cerebral ischemia and cerebral infarction based on tear Raman spectroscopy combined with multiple machine learning algorithms.

Researchers have established a classification model based on tear Raman spectroscopy combined with machine learning classification algorithms, which realizes rapid noninvasive classification of cerebral infarction and cerebral ischemia, which is of great significance for clinical medical diagnosis. Through spectral data analysis, it is found that there are differences in the content of tyrosine, phenylalanine, and carotenoids in the tears of patients with cerebral ischemia and patients with cerebral infarction. We try to establish a classification model for rapid noninvasive screening of cerebral infarction and cerebral ischemia through these differences. The experiment has four parts, including normalization, data enhancement, feature extraction, and data classification. The researchers combined three feature extraction methods with four machine classification models to build a total of 12 classification models. Integrating 8 classification criteria, the classification accuracy of all models is above 85%, especially PLS-PNN has achieved 100% accuracy and better running time. The experimental results show that tear Raman spectroscopy combined with machine learning classification model has a good effect on the screening of cerebral ischemia and cerebral infarction, which is conducive to the noninvasive and rapid clinical diagnosis of cerebrovascular diseases in the future.