## 4 Discussion  
Dementia patients are at a greater risk of harming themselves and others during episodes of aggression. The risk factors involved in these behaviors have been widely studied. One vital goal is to reduce caregiver stress by informing them of the risks involved in these behaviors. Additional investigation of the interaction among different cases of behaviors has been accomplished through examination of vocally and physically aggressive and non-aggressive behaviors in order to better understand the factors related to agitation and aggressive behaviors among dementia patients. Findings showed that verbal aggression was most disruptive [44, 45]. In this project, a machine learning model was designed, trained, and evaluated, capable of detecting and classifying aggressive and non-aggressive human behavior based on visual and audio data. To complete this proof-of-concept study, two meta-classifier fusion methods were proposed: the Early and Late Fusion models. This multimodal combination of previous works will further contribute to the competencies of this proof of concept and predict episodes of aggressive behavior in dementia patients more accurately and with improved reliability. As in the prior work, limitations in acquiring clinical datasets of real dementia patients are a drawback due to ethical concerns as well as patient consent. Partnerships with psychiatric hospitals are necessary to acquire such data and verify the applicability of the model in real-life scenarios. A proof of concept established the basis for the model to be created to simulate real-life scenarios, increase model reliability, and prepare it for clinical use, and enable future model validation with clinical data [46, 47]. **Dimensionality of Data**  
In multimodal datasets, a dataset with high dimensionality plays a crucial role when it comes to providing a rich and analytical representation of aggressive behavior prediction. The incorporation of visual and audio cues in this multimodal analysis benefits both models by providing enriched and complementary data sources. This allows a more analytical, comprehensive view of the classification and prediction of aggressive behaviors. Multimodal fusion also enhances the potential abilities of the model to learn from new data, since variability in modalities provides unique insights. This approach comes with a key advantage, which is the model's ability to handle missing data, therefore providing a more robust approach. In case of missing data, such as verbal audio data or missing or distorted images, the model can still provide meaningful  
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predictions from available data. This highlights the robustness of this approach and ensures a more stable and reliable classification process. Moreover, the integration of deep-level interactions in features between audio and visual data helps the model to develop a better understanding of aggression patterns. **Interpretation of Results**  
Comparative analysis in this research between Early and Late Fusion approaches highlights the strengths and trade-offs of each method. The Late Fusion model outperformed Early Fusion in the majority of the evaluation metrics, including recall, accuracy, F1-score, ROC-AUC score, and inference time, while at the same time, no significant statistical differences were present between the comparison of the two models. This suggests that the aggregation of data of different natures in decision-level predictions provides a more robust process for real-world clinical applications. Despite being outperformed by Late Fusion, Early Fusion exhibits better precision metrics with a low rate of false positive values, suggesting that it is better at avoiding false detections. This improvement comes with the trade-off of a higher risk of missing aggressive occurrences due to its low recall score. This showcases the importance of selecting a fusion strategy that is best suited for the requirements of the environment in which it is needed to perform.