## 4 Results and Discussion
### Machine Learning Models
Figure 5: The confusion matrices of the developed models. (**a**) BT model; (**b**) NN model; (**c**) SVM model. The results presented in Figure 5a are based on a model developed using the bagged tree method. The matrix presents the percentages of correct and incorrect classifications for six different classes, labelled D1 through D6. Cells along the main diagonal of the matrix indicate the correct classifications. For example, the model achieved 96.2% accuracy for class D1, while the accuracy for D2 was 90.3%. The highest level of precision was observed for class D5, with 97.2% of cases correctly classified. In contrast, the least accurate classifications occurred for class D2. Additionally, the misclassifications are illustrated in the off-diagonal cells. For example, 3.2% of the instances belonging to class D1 were incorrectly assigned to class D2, while 4.5% of the cases classified as D3 were, in fact, classified as D2. Furthermore, the positive predictive value (PPV), which gauges the accuracy of classification for each class, and the false discovery rate (FDR), which quantifies the proportion of erroneous predictions, are also presented. The highest level of precision was observed for class D5, with a value of 97.2%, while the lowest level of precision was observed for class D2, with a value of 90.3%. Similarly, class D2 exhibited the highest FDR at 9.7%, indicating that predictions for this class were more susceptible to error. On the contrary, class D5 exhibited the lowest FDR at 2.8%, demonstrating the highest classification precision. Overall, the Bagged Trees model demonstrates robust performance, although further optimisation is necessary for classes such as D2 to reduce misclassification rates. The confusion matrix for the NN model (Figure 5b) indicates a high degree of accuracy, with classes such as D3 and D5 achieving the highest results at 97.4% and 97.2%, respectively. Class D1 is correctly classified 95.2% of the time, while class D2 achieves an accuracy of 92.2%. The highest frequency of misclassification is observed for D6, with 9.0% of instances incorrectly predicted, predominantly as D1. The misclassification rates for the remaining classes are relatively moderate, with 4.5% of instances of class D1 being misclassified as class D6 and 3.6% of instances of class D2 being predicted as class D4. Furthermore, the precision (PPV) and false discovery rate (FDR) metrics provide further insight into the model's strengths and weaknesses. D3 and D5 demonstrate the highest precision at 97.4% and 97.2%, accompanied by low FDR values of 2.6% and 2.8%, respectively. This indicates a robust capacity to accurately identify these classes. However, D6 exhibits the lowest precision at 91.0% and the highest FDR at 9.0%, indicating that this class is the most challenging for the model to classify accurately. Although the overall performance of the neural network is strong, additional refinement, particularly for class D6, could help improve the model's prediction accuracy and reduce misclassification rates. The confusion matrix for the SVM model (Figure 5c) indicates a high level of accuracy across most classes, particularly for classes D1, D4, D5, and D6, where the model achieved a perfect classification rate of 100%. However, the performance for class D2 is notably less accurate, with an accuracy of 84%, while class D3 shows a correct classification of 94.3%. The highest frequency of misclassification was observed for class D2, with 16% of instances incorrectly classified as class D1, and for class D3, with 5.7% of instances incorrectly classified as class D2. Despite these misclassifications, classes D4, D5, and D6 demonstrate no misclassification whatsoever, thereby underscoring the robustness of the model with respect to these categories. In terms of the precision (PPV) and false discovery rate (FDR), the model demonstrated remarkable performance for classes D1, D4, D5, and D6, achieving 100% precision and no false discoveries for these classes.