## 4 Discussion
### Ethical Considerations  
Considering the wider moral ramifications of machine-learning algorithm applications for predicting aggressive behaviors in vulnerable populations, we must address related ethical issues, such as privacy and misclassification risk.  
**Privacy:** Predictive models frequently use sensitive data, including audio biometric data, pictures, video records, and behavioral logs. We must abide by stringent privacy regulations and ethical standards to prevent abuse or unlawful usage. All the datasets and participants must remain anonymous, and the participants must provide their agreement after being fully informed about how their data will be used. Strict data privacy is another crucial responsibility of researchers, particularly for vulnerable populations [60,61];  
**Risk of False Positives and False Negatives:** False positives could have a number of negative effects, including unwarranted medical intervention or stigmatization, as a result of incorrect predictions of aggressive conduct. These could lead to incorrect actions or have a detrimental impact on the health of individuals who are already at risk. Clinical decision making is supported by the use of predictive models. Such dangers can be reduced by combining machine learning with the knowledge of skilled employees [62,63];  
**Bias and Fairness:** Biases in training data can be applied by algorithms, leading to incorrect findings. When working with communities or vulnerable populations, this is particularly troublesome. When creating such models, mitigation techniques are incorporated. Regular actions must be taken to guarantee the fairness, transparency, and minimization of bias in such systems [60,61]. Considering misclassification and bias mitigation in the datasets, both datasets were not balanced from their sources. To mitigate bias and fairness, cross-validation techniques with 5 \(\times\) 5 folds were used to eliminate randomness and reduce misclassification risks;  
**Ethical Deployment and Oversight:** Prior to putting such models into practice in community contexts, like psychiatric hospitals, schools, or prisons, it is imperative to involve interdisciplinary and mental health teams of experts. To prevent unnecessary risks to individuals who are already in danger, the morality of these models must be ensured [60,61];  
**Accountability and Human Agency:** The purpose of machine-learning models is to assist human judgment. Case-by-case use should be left to experts, who can understand the model's behavior and predictions in a broader context. When working with vulnerable groups, formal accountability structures should be implemented to ensure such functionality;  
**Patient Consent and Privacy:** The dataset was acquired from Kaggle's \"Audio-based Violence Detection Dataset\" a free open-source dataset, with audio files ready for academic use. No consent form was needed because of the dataset's nature. No ethics board or institutional review board (IRB) approval was needed or engaged in this study. The data (both visual and audio) were already anonymized from their sources without posing any potential risk or harm through the intended use. The dataset was publicly available and pre-existing, with no direct interaction with patients, and did not contain any personally identifiable information (PII). To prevent unethical bias in data cross-validation, 5 \(\times\) 5 folds were utilized in all our models current and prior, to eliminate random predictions.