## 3 Results
### PCA-LDA-Based SVM  
We used PCA-LDA-based SVM to classify three different groups: POAG, PACG and normal groups. Two experiments were conducted to demonstrate the efficiency of our model:  
Experiment A: The training and test dataset are the same and consist of all data. Experiment B: All data are split to generate the training dataset (70%) and test dataset (30%). Experiment A and Experiment B were completely independent experiments with no influence between the settings and results of the two experiments. We conducted Experiment A to initially verify the validity of our model. However, there was no impact on Experiment B. Experiment B was divided into training and test sets, which strictly avoided \begin{table}
\begin{tabular}{c c c} \hline \hline
**Raman Shift (cm\({}^{-1}\))** & **Tentative Assignment** & **Molecular Origin** \\ \hline
447 & Ring torsion & Phenyalalanine \\
760 & Ring breathing mode & Tryptophan \\
853 & Ring breathing mode & Tyrosine \\
879 & & Hydroxyproline, tryptophan \\
1003 & & Phenyalalanine \\
1033 & & Phenyalalanine \\
1270 & & Amide III \\
1313 & CH3 CH2 twisting mode & Collagens and lipids \\
1582 & & Phenyalalanine \\
1664 & & Amide I \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Main Raman peak assignments in tears of glaucoma patients and healthy individuals [30; 38; 43]. Figure 3: Specific intensity comparisons of Raman peaks from POAG, PACG and normal groups. (**a**) The spectral range of 620–950 cm\({}^{-1}\). (**b**) The spectral range of 1200–1750 cm\({}^{-1}\). The standard deviations were listed for each peak (\({}^{\star}\)_p_ < 0.05). data leakage. Therefore, the results of Experiment B can fully reflect the performance of the proposed PCA-LDA-based SVM model. Figure 4a depicts the classification result of Experiment A with a scatter diagram, which shows that the model can provide an apparent distinction of different groups. Table 3 lists the related evaluation parameters for the classification results of Experiment A and Experiment B. The classification accuracies of the PCA-LDA-based SVM diagnostic model were 93.2% and 90.9% for Experiment A and Experiment B, respectively. For Experiment A, the correction rates of the model correction for POAG, PACG and normal groups were 88.9% (24/27), 89.5% (17/19) and 100% (27/27), respectively, with a total accuracy of 93.15%. The specificities of the classification for POAG, PACG and normal groups were 97.8% (45/46), 94.4% (51/54) and 97.8% (45/46), respectively. For Experiment-B, the sensitivity and specificity of the differentiating POAG group were 80.0% (4/5) and 100% (17/17); for the PACG group classification, they were 87.5% (7/8) and 92.9% (13/14). The sensitivity and specificity of the normal group classification were 100% (9/9) and 92.3% (12/13). To further evaluate the model with sensitivity and specificity, the receiver operating characteristic (ROC) curve was generated to illustrate the diagnostic ability of the model (Figure 4b). The area under the curve (AUC) can directly reflect the classification performance based on cross-validation results. The AUCs of POAG, PACG and normal groups were 0.99, 0.97 and 1.0, respectively. Then, micro-average AUC and macro-average AUC were also used for evaluation. Micro-average AUC was defined as the overall AUC related to all categories. First, a series of confidence thresholds \(\{\rm t_{1},t_{n},\ldots,t_{n}\}\) were determined. Second, the overall false positives and true positives were calculated without categorization  
Figure 4: Classification results for POAG, PACG and normal groups with the PCA-LDA model. (**a**) The scatter diagram of the three groups. (**b**) The receiver operating characteristic (ROC) curves for the classification of three groups.