Serum Raman Spectroscopy Combined With a Support Vector Machine for Rapid Diagnosis of Hepatitis B and Liver Cirrhosis

ABSTRACT Hepatitis B (HB) and hepatitis B related liver cirrhosis (LC) severely threaten global public health. Early and accurate differentiation of HB, LC, and healthy controls (HE) is crucial for optimizing treatment and improving prognosis. This study demonstrated that serum Raman spectroscopy combined with support vector machine (SVM) enables rapid diagnosis of HB and HB‐induced LC. We established a Raman spectral database of 78 HB cases, 73 LC cases, and 81 HE cases, adopting a nested fivefold cross‐validation (fivefold CV) scheme: Hyperparameters were tuned via grid search (internal fivefold CV) on the training set, with final performance estimated on an independent external test set. The entire process was repeated 10 times with different random seeds, and results were macro‐averaged. The three‐group classification achieved an overall accuracy of 78% and a macro‐average area under the curve (AUC) of 91%, outperforming random forest (RF), principal component analysis–linear discriminant analysis (PCA‐LDA), and k‐nearest neighbors (KNN). This study confirms the method's great potential for noninvasive screening of HB and LC.