###### Abstract  
This study presents an enhancement in the prediction of aggressive outbursts in dementia patients from our previous work, by integrating audio-based violence detection into our previous visual-based aggressive body movement detections. By combining audio and visual information, we aim to further enhance the model's capabilities and make it more suitable for real-world scenario applications. This current work utilizes an audio dataset, containing various audio segments capturing vocal expressions during aggressive and non-aggressive scenarios. Various noise-filtering techniques were performed on the audio files using Mel-frequency cepstral coefficients (MFCCs), frequency filtering, and speech prosody to extract clear information from the audio features. Furthermore, we perform a late fusion rule to merge the predictions of the two models into a unified trained meta-classifier to determine the further improvement of the model with the audio integrated into it with a higher aim for a more precise and multimodal approach in detecting and predicting aggressive outburst behavior in patients suffering from dementia. The analysis of the correlations in our multimodal approach suggests that the accuracy of the early detection models is improved, providing a novel proof of concept with the appropriate findings to advance the understanding of aggression prediction in clinical settings and offer more effective intervention tactics from caregivers. machine learning; multimodal analysis; late fusion; meta-classifier; dementia; aggressive outbursts 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vol., pp. 2020-2020, 2  
is most commonly associated with dementia and can manifest both in verbal and physical aggression episodes and agitation [1]. According to findings from studies, 50% of dementia patients exhibit at least one form of aggressive behavior, which can be harmful to caregivers and mental healthcare workers, as well as patients. Aggressive behaviors can be triggered by frustration, confusion, pain, or environmental changes. Such behaviors can pose significant risks to patients themselves and healthcare employees, in turn, increasing stress and emotional burden. Patients sometimes may harm themselves and cause injuries, episodes that increase the patient's condition over time [2]. The early prediction of these symptoms, before their escalation, presents a significant objective because it can provide the opportunity for timely interventions that can reduce the severity of these episodes, thereby reducing distress for patients and caregivers. Timed interventions can also de-escalate high risk situations and provide time to formulate treatment plans and preventive measures, hence decreasing harm and distress while reducing pharmacological treatment. Previous research has relied heavily on visual cues to detect and predict such aggressive behaviors. By integrating audio-based feature analysis, a more robust prediction method can be provided based on multimodal analysis while increasing prediction accuracy [3]. The mini-mental state examination (MMSE) is commonly used in cognitive impairment screening, with limited effectiveness in dementia progression prediction in patients suffering from mild cognitive impairment (MCI). Researchers, in a review of 11 studies with approximately 1569 patients suffering from MCI, found that the MMSE is not accurate in predicting conversion to dementia, indicating the need for multiple longitudinal assessments [4]. In a review, researchers analyzed 52 cognitive assessment tools commonly used for the identification of mild cognitive impairment (MCI) in its early stages. This review identified the Montreal cognitive assessment (MoCA), mini-mental state examination (MMSE), and clock-drawing test (CDT) as the most cited techniques, with the six-Item cognitive impairment test (6 CIT) and the Hong Kong brief cognitive test (HKBC) as the most recommended, taking into account various factors, such as age and education level [5].