## 1 Introduction
### Risk Factors  
The term risk refers to the probability that an event, generally unfavorable, will occur within a given period of time or before a given age. A risk factor may be an aspect of personal behavior or lifestyle, an environmental circumstance, or due to an inherited characteristic. This is not necessarily a causal factor, but it is a marker of increased probability [20]. All biological alterations associated with the individual are classified as intrinsic factors of falls. However, due to the multifactorial nature of these events, those factors that are extrinsic must also be considered, that is, they include the environment [21]. Intrinsic factors are specific to each individual and include age, chronic diseases, muscle weakness, problems with gait, balance, and cognitive impairment [21]. Extrinsic factors link the etiology of falls with psychosocial behavior and the environment in which people live (for example, environmental hazards or dangerous activities). The latter is described as the leading cause of falls and accounts for approximately half of all falls [22]. Conditions such as walking on slippery/rough surfaces, obstacles, inadequate lighting, loose carpets, or even the way medications are taken at home [23] promote tripping or slipping in any age group, but they represent a significant risk in people with underlying intrinsic factors. To assess fall risk, multiple tools and functional evaluation tests have been developed. Among these, the Mini-Balance Evaluation Systems Test (Mini-BESTest) has proven to be one of the most effective methods for evaluating postural control and balance in older adults [24; 25]. This test assesses the key components of dynamic balance through 14 items addressing four main domains: anticipatory control, reactive control, sensory orientation, and gait stability. Several studies have demonstrated the ability of the Mini-BESTest to predict falls; however, its use for the automatic classification of fall risk remains limited. Manual evaluation of these methods requires clinical expertise, making their large-scale, automated implementation difficult. With the advancement of Artificial Intelligence (AI) and deep learning, new opportunities have emerged to automate and improve fall risk prediction methods in this population. Deep learning techniques, which mimic the functioning of the human brain through artificial neural networks, allow for the processing of large volumes of complex data and the extraction of patterns not apparent through traditional methods. These techniques have been widely applied across various areas of medicine, including disease detection, medical image analysis, and clinical outcome prediction. Currently, efforts are focused on developing new paradigms for fall prediction in older adult populations, aiming to integrate both real-time assessment and the risk of future falls. These approaches are crucial for optimizing the quality of healthcare systems, as the accurate identification of fall risk is an essential prerequisite for their effective implementation [26; 27]. On the other hand, the ability to anticipate and proactively address the phenomenon of falls requires predictive tools that are robust and efficient, as well as capable of detecting older individuals who are at a high risk of falling. Furthermore, early intervention, based on proper forecasting, is critical to mitigating the incidence of this adverse event and facilitating the appropriate care and preparedness for this vulnerable population [26; 28]. In this context, the present study aimed to classify risk of falls in community-dwelling older adults based on patterns derived from the Mini-BESTest using a deep neural network approach. Our main hypothesis was based on the fact that the Mini-BESTest scoring patterns provide key information to identify individuals at risk of falls and that a deep learning model improves the classification accuracy compared to traditional methods.