## 4 Discussion
Considering a fall prevention point of view, these results may suggest that older adults with this type of functionality pattern could be approached similarly from an operational point of view, thus facilitating a collective approach process. On the other hand, this study encourages a different approach to older adults who self-report two or more falls within 6 or 12 months and are considered recurrent fallers. It isbelieved that these individuals may present more striking functional and clinical characteristics and could be efficiently identified by this unsupervised self-organization algorithm. The secondary aim of this study was to determine which outcomes better discriminate community-dwelling older adults with and without self-reported falls by an unsupervised algorithm. The second premise of this study was confirmed, especially for variables that denote an increase in the cost of cognitive demands regarding a motor task (TUG) or in concurrent cognitive tasks (such as TMT A and B or Stroop test versions Black and White versus Colored). These outcomes proved to be capable of signaling a better grouping capacity of the participants in this study. However, it is worth highlighting again that based on the sensitivity, precision, F1-score, and accuracy metrics, these groups are not necessarily fallers and non-fallers as expected. With this result, it is only possible to infer that these variables could divide the study participants into two groups with distinct motor-cognitive characteristics. It is essential to highlight that SOM is an unsupervised learning algorithm, i.e., an ML algorithm designed to initially deal with data sets in which samples do not have previously assigned labels or categories. Unlike supervised algorithms, in which the training data includes labeled input-output pairs, unsupervised algorithms explore the internal structure of the data to discover whether there are possible hidden patterns, clusters, or relationships without external guidance. The primary purpose of an unsupervised algorithm is to explore the intrinsic structure of the data and find useful information without the need for pre-defined labels or categories [13]. However, we chose to complement this study with metrics such as sensitivity, precision, F1-score, and accuracy that could present the performance of the SOM compared with the labels initially assigned to participants based on self-reported falls in the 12 months preceding the study. On the other hand, the outcomes that denote functional performance related to postural balance did not show discriminatory capacity among the participants of this study, signaling that they may not be the best way of distinguishing older adults with and without self-reported falls. A systematic review on the use of the Functional Reach Test (FRT), Single-Leg Stance Test, and Tinetti Performance-Oriented Mobility Assessment (POMA) to predict falls in older adults showed that these clinical tests of postural balance should not replace a comprehensive assessment of the risk of falls and, therefore, should be incorporated into practice only to identify and track balance impairments in older adults [4]. In the same field of research, another study [7] employed SOM and k-means clustering (KM) to determine which gait characteristics best cluster individuals based on their age groups, which ranged from 10 to over 50 years old. The researchers demonstrated that these ML approaches efficiently clustered the main gait features based on the age of the participants. According to the authors, these findings can be helpful, especially for inexperienced professionals unlikely to identify subtle differences in walking metrics, aiming to recognize functional ability and physical performance [7]. Thus, these clustering methods can be valuable in supporting and guiding healthcare practitioners' evaluation and intervention processes based on age, history of falls, and functional or cognitive functioning.