## 4 Discussion
**Real World Application Strategies**  
Quality control is another crucial consideration when looking at actual clinical data. The quality of real-world data is impacted by missing values and incorrect labeling and usability. In order to achieve this, mitigation strategies have already been applied in every model under study. Robust data augmentation techniques that mimic clinical data noise in training and domain adaptation methods that use fine-tuning to transfer learning from a controlled dataset to a clinical dataset are examples of adaptation strategies that can be put into practice in response to the challenges discussed. Lastly, cooperation with healthcare organizations is required to obtain clinical data from mental health facilities and to engage in active learning within an active feedback loop where human specialists can oversee and determine the reliability of the results. **Model Robustness and Clinical Application Challenges**  
It is important to explore potential challenges to the implementation of the model in real clinical environments. Specifically, background noise issues, patient diversity, and missing sensor data are all difficulties that would likely have significant impacts on the model's performance. To further enhance our model's robustness, future work will entail testing the model in noisy environments, with artificially injected noise during training. This will challenge the model's ability to perform in less-than-perfect conditions, common in clinical practice. In addition, behavioral and patient heterogeneity must be examined. The model's effectiveness would rely on some patient characteristics, like age, cognitive status, and history of behavior. This issue could be addressed through the implementation of solutions such as transfer learning, whereby the model will learn to generalize to new settings and datasets without the need for lengthy fine-tuning. Early Fusion's high precision advantage, as demonstrated in this study, also needs to be considered in the context of specific clinical circumstances. While Early Fusion is better at avoiding false alarms, this needs to be balanced against the clinical need to detect aggressive behaviors in a timely manner. The cost of reducing false positives in the clinical setting must be weighed carefully against the cost of missing an aggressive incident. Futureresearch would have to address the clinical usefulness of such a trade-off, concerning real-world contexts in which false positives and false negatives both have direct implications for patient safety and care. **Ethical Considerations**  
Given the broader ethical implications of using machine learning algorithms to forecast violent tendencies in susceptible groups, we have to discuss associated moral concerns like the risk of misclassification and privacy. * **Privacy:** Sensitive information, such as audio biometric data, images, video recordings, and behavioral logs, is commonly used in predictive models. To stop misuse or illegal use, we must adhere to strict privacy laws and moral principles. Participants must give their consent after being fully informed about how their data will be used, and all datasets and participants must remain anonymous. Another essential duty of researchers is strict data privacy, especially for vulnerable populations [49; 50];
* **Risk of False Positives and False Negatives:** False positives may result in unnecessary medical treatment or stigmatization due to inaccurate forecasts of aggressive behavior. These might result in poor decisions or negatively affect the health of those who are already at risk. Predictive models are used to support clinical decision-making. Combining machine learning with the expertise of knowledgeable staff members can lessen these risks [51; 52];
* **Fairness and Bias:** Algorithms may apply biases in training data, producing inaccurate results. This is especially problematic when working with vulnerable populations or communities. Mitigation techniques are used when developing such models.