## 4 Discussion  
Patients with dementia can exhibit aggressive behaviors towards caregivers and mental health institutes, increasing the risk of harm to themselves and others. Various studies have examined the factors related to these behaviors. A primary focus is to assert the dangers of these behaviors thereby reducing stress to caregivers [16; 18]. In order to better understand factors involved in agitation and aggressive behaviors in patients with dementia, further examination of the relationship between various types of behaviors has been undertaken by studying physically and verbally aggressive and non-aggressive behaviors. The results showed that the most disruptive type of aggression was verbal aggression [17]. Factors that cause aggressive behaviors and early signs that portend aggressive behavior in patients with dementia have previously been recognized. Hyperactivity, persecutory ideas and sad appearances can occur prior to an aggressive episode [8]. In emergency departments, dementia is a common condition among elderly patients. Patients exhibit psychotic symptoms, disturbances in motor activity and aggression. One study concluded that there are specific body movements from these patients anteceding an aggressive outburst [46]. Machine learning application approaches have been applied in psychiatry such as PTSD diagnosis [6], early stage dementia diagnosis and MCI, as well as First-Episode Psychosis using novel approaches such as multilayer perceptron and Support Vector [9; 47]. Thus far, there are no studies in the field predicting aggressive behavior using machine learning models utilizing body pose and facial features expressions. In this study, we developed, trained and validated a machine learning model that is capable of detecting and classifying argumentative and non-argumentative behaviors between two individuals in video frames. This foundational model is presented as a potential application for preventing aggressive outbursts in dementia patients. Limitations in acquiring datasets from dementia patients present a hurdle in the context of ethical concerns and patient consent. Collaborations with partners from the psychiatric section are needed in order to test this proof of concept model by obtaining access to clinical data from patients suffering from dementia. This will allow us to evaluate the model in real-world scenarios and enhance the robustness and reproducibility of our findings in subsequent research. The final model has high precision (**90.18%**) [39], translating to the high reliability of positive predictions and equal representation of classes in fair evaluation due to its balanced dataset. However, the model has a moderate recall (**70.08%**), a low NPV score (**75.53%**) and a substantial decrease in performance from training to testing. The reasons for the latter include overfitting or data discrepancies [31]. Among the strengths of our model are high classification metrics pointing to high effectiveness in distinguishing between two classes and its robustness to different operating conditions, performing well even under different decision thresholds (Figure 15) [39]. Further, the model exhibits high precision in positive predictions and can accurately distinguish the two separate classes of behaviors with false positive rates remaining low. Ensemble classification models (**Random Forest Classifier/Gradient Boosting**) are appropriate in order to avoid overfitting and thereby contribute to the overall performance of the model. Its adaptability to different operating conditions and consistent performance in various decision thresholds makes it a good launching pad for potential clinical applications in detecting and predicting aggressive behaviors in dementia patients, which could improve intervention strategies employed by caregivers and management. Regarding the model's weaknesses, these pertain mostly to the model's metrics.