## 5 Conclusions  
In this study, the main goal was to create a secondary classifier model based on audio data, which will enhance the prediction capabilities of our initial visual model in predicting aggressive outbursts in dementia patients. Using the same methodology as in the initial visual model, an audio model was developed, trained, and tested using data noise and artifact filtering, imputation of missing data, and standardization. Following the initial results, merging the prediction results of the two models followed, using the late fusion technique and feeding the final dataset into a meta-classifier. The meta-classifier was trained and tested on the combined data types (audio and visual) and showcased improved prediction results. As more complex and differentiated data were introduced to the model, a more realistic real-world application scenarios could provide insights about the future of this multimodal analysis.  
It should be taken into consideration that controlled conditions were used in the previous work. Real-world environmental data often contain noise and variability. In this research, data of such a nature were introduced, with audio clips containing information about various daily life instances. Audio noise and artifacts were filtered with real-life variability. Even though the nature of the data was not controlled in this model development, the results nonetheless improved. This could be a strong indication that our proof of concept could be tested under real-world conditions and be tested using real clinical data.  
For future work, the model could be implemented with an early fusion rule, in order to cross-validate the results and the model's capabilities. Also, the acquisition of a real-world clinical dataset will further indicate utilization in providing a novel approach for advanced care in dementia patient facilities, encompassing a greater variety of noise and behaviors in its functionality. Although the model provides promising results, stability issues may occur because of the small size of the training data sample. Another important step toward future improvement would be an early fusion model. In this model, prediction merging from the audio and visual models would merge features in a unified concatenated dataset. This integration could provide a better representation and lead to an improved understanding of the classification performance. This proof of concept will provide useful insights about the future implementation of the model, its generalizability skills, and its resilience, opening a new door for machine learning in psychiatric applications and healthcare systems while also improving patients' qualities of life.  
Conceptualization, I.G.; Methodology, I.G., R.F.S. and N.K.; Software, I.G.; Validation, I.G.; Formal analysis, I.G.; Investigation, I.G. and A.V.; Resources, I.G.; Data curation, I.G.; Writing--original draft, I.G.; Writing--review & editing, I.G., R.F.S., N.K. and M.S.; Visualization, I.G.; Supervision, I.G. and I.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original data presented in the study are openly available at [[https://www.kaggle.com/datasets/fangfangz/audio-based-violence-detection-dataset](https://www.kaggle.com/datasets/fangfangz/audio-based-violence-detection-dataset)]([https://www.kaggle.com/datasets/fangfangz/audio-based-violence-detection-dataset](https://www.kaggle.com/datasets/fangfangz/audio-based-violence-detection-dataset)) (accessed on 15 January 2025).  
The authors declare no conflict of interest.