# Multimodal Classification of Anxiety Based on Physiological Signals
## 5 Discussion
### Anxiety Classification  
Concerning the multimodal classification of anxiety, another challenge is that, frequently, in a non-clinical population, more severe cases of anxiety are rare [1]. Nevertheless, training machine learning models to predict those levels accurately is of crucial importance as they represent higher health risks. The WESAD dataset [1] used in this work is imbalanced, as more severe cases of anxiety are not as common, and consequently, the various levels are not equally represented. As this poses difficulties for standard classification algorithms, we resorted to techniques that involve oversampling and synthetic data generation to improve anxiety classification. Overall, for both F1 and ROC-AUC, a pattern can be distinguished in the global efficacy of the balancing techniques, which reveals a better performance with Borderline SMOTE 2 and ADASYN. In Table A3, it can be seen that Borderline SMOTE 2 attained the highest results in both metrics with two classifiers--LR and SVM in F1 with DT and ROC-AUC with LDA and RF.SMOTE was the best technique with two classifiers--in both metrics with ADB and F1 with RF. ADASYN outperformed the other techniques with XGB and ROC-AUC with DT. Random oversampling was the least efficient technique in both metrics, achieving only the best results in F1 with LDA. Ordering the combination of data balancing techniques, considering the number of classifiers that obtained the best results, we get the following sequence: Borderline SMOTE 2, ADASYN together with SMOTE, and Random Oversampling. The success of Borderline SMOTE 2 and ADASYN can be explained by the way they synthesize new instances in an informed rather than random manner, acting on the examples that are more likely to be misclassified. Our results support a comparison study [39] that used Random Oversampling, SMOTE, ADASYN, and Borderline SMOTE 1 and 2. For both true positive rate (TPR) and F1, Borderline SMOTE 1 and Borderline SMOTE 2 performed better than the other data balancing techniques. Analyzing F1 and ROC-AUC metrics, the balancing techniques have been shown to improve, in general, the classification results. Moreover, in some cases, the addition of class overlaps, noise, and even overfitting of the classifiers prevented the enhancement of the results. Borderline SMOTE 2 showed the most consistent results. Regarding precision and recall, there is a notable overall pattern of increased values. However, recall is consistently higher than precision, which is valuable since misclassifying severe cases of high anxiety is more harmful. In cases where the data balancing did not improve F1, both metrics decreased or precision worsened considerably. As a final remark, concerning the balancing techniques, the \"default\" sampling rate of 1:1 was used, which might not always be the best option for every classifier. It would be beneficial to tune the sampling ratio on our dataset while considering each classifier at a time. Since the same parameters were chosen for each balancing technique and applied to all classifiers, some classifiers may respond better to the parameters selected, which can also partially explain the differences encountered in the results for each technique. Although our study uses only the data collected during the neutral condition, and therefore comparisons of the obtained results to anxiety-stimulating conditions may be inaccurate, we can observe the results of the benchmark study performed on the WESAD dataset [1]. In the binary classification task (stress vs. non-stress, or neutral and amusement conditions), the best F1 scores using the RespiBAN physiological modalities--ECG, EDA, EMG, RESP, and TEMP--were obtained by the RF, ADB, and LDA classifiers for the binary task and ranged from 86.0% (RF) to 90.9% (LDA) [1]. For our study, the best F1 results were obtained by ADB, RF, and XGB, ranging from 84.5% (RF) up to 86.4% (ADB). Our F1 results are close but slightly worse than the results from the benchmark study, even though we are solely working with the neutral condition.