## 4 Discussion
**Performance Behavior Across Data Conditions**  
The observed difference in performance between Early and Late Fusion models is explained through an examination of how each of the fusion approaches responds to sample-specific modality information under varying conditions of samples. Early Fusion combines audio and vision features at the input level in the form of a concatenated single feature vector. This approach enables the model to learn representations together. It also introduces the risk of modality imbalance, whereby dominant or noisy features from a single modality (like irregular audio information or poor-quality visual frames) can obscure informative patterns from the competitor model. This phenomenon is especially significant in borderline or uncertain samples, adding to Early Fusion's decreased recall, due to the fact that it struggles to respond to minimal cues of aggression. Late Fusion, in contrast, has modality-specific decision boundaries with different classifiers and fuses their decisions at the decision level. The design of Late Fusion allows for the exploitation of asynchronous modality salience. When there is only a single modality that provides a strong signal (e.g., clear speech tone changes even when visually occluded), the classifier can nevertheless trigger a correct aggressive classification. This results in better recall and improved ROC-AUC, particularly in edge cases. The steeper slope of its learning curve up to 210 samples also suggests that modality-specific classifiers are more data-efficient at initial stages, and therefore have more discriminatory within-modality structures, and are quicker to converge with fewer samples. Moreover, Early Fusion's increased accuracy is the result of higher decision thresholds that are a byproduct of early integration. As predictions are based on a combined feature space, only samples with clear, consistent signals across both modalities are labeled as aggressive. This reduces false positives but increases false negatives, as seen from the confusion matrix. This is especially critical where minimizing false alarms is of primary concern (e.g., autonomous surveillance), but not ideal in high-stakes contexts where missing an aggressive incident can undermine safety. Lastly, the performance gap is non-uniform but data-based, with Early Fusion performing relatively better in clean and well-aligned input situations and Late Fusion dominatingnoisy, heterogeneous, or modality-damaged situations, bearing testament to its resilience and adaptability. **Choice of Methodology**  
Based on earlier research, the pipeline was chosen for this study and stayed the same in order to adhere to the model's hierarchy and produce reliable results. Consequently, the Random Forest classifier's ensemble classification feature was repurposed since it is the best method for the application due to its versatility in operating conditions and consistent performance across a range of decision thresholds [48]. Alternative classification model options could be used in this study, but doing so would render the improvement's purpose moot because a different classifier would not offer consistency or dependability for a later comprehensive application of the model in real-world situations. **Real-World Clinical Settings Challenges**  
The use of controlled and/or simulated datasets and their effects on actual clinical settings must be discussed. As was previously mentioned, the datasets used in the audio and visual models came from open-source, free online datasets. These helped create the models and produce a proof-of-concept model that could be subsequently tested in actual clinical settings. In future research, these models can be further enhanced using clinical audio and visual data. Clinical datasets may reveal issues that could affect the type of results, like background noise and other environmental noise present in clinical settings. Further, since every patient has different behaviors, individual variability may impede predictions.