## 4 Discussion  
The first purpose of this study was to map a high-dimensional and complex clinical and functional dataset from community-dwelling older adults with and without self-reported falls in the previous 12 months. The premise concerning this aim was that the self-organizing mapping would accurately identify the participants with and without self-reported falls based on these data. However, although the main results of the model achieved a sensitivity of 77% in identifying participants with a self-report of a single fall, the accuracy was roughly 50%. This accuracy percentage demonstrates that the proportion of cases correctly classified by the model regarding the total number of cases was close to a random classification. Although the clinical and functional data considered in this study allowed the SOM to make the topological distinction between the two groups, these results suggest that the participants were not necessarily \"fallers\" and \"non-fallers\" as previously labeled based on self-report of a single fall in the last 12 months. Hence, these results lead us to refute the first premise of this study. A possible explanation for the accuracy of approximately 50% may have been the imbalance between the number of participants in the two groups, as the group of fallers consisted of 22 participants while the non-fallers group contained 38 older adults. Although this imbalance could negatively interfere with accuracy, this proportion is representative of the prevalence of fallers in the general population, especially in Brazil [38]. However, when analyzing the results of models with balanced samples (22 fallers and 22 non-fallers), no significant improvement in the magnitude of accuracy was observed. Furthermore, the F1-score value was not significantly modified with or without the imbalanced sample. It is worth highlighting that the F1-score (the harmonic mean of precision and sensitivity) is a valuable metric in classification problems with sample size imbalances as it considers both precision and recall, providing a more balanced view of model performance regardless of class imbalance. Therefore, the model with a sample imbalance was maintained, as it represents the proportion of fallers and non-fallers commonly presented in the literature [38]. Given the encouraging findings of this study, we recommend applying this model to a more extensive database, even though the metrics and results of this study have confirmed the SOM was effectively able to recognize and cluster the presented patterns, even in a relatively modest but adequate sample size. A conventional statistical method (\(t\)-test) was used to validate the SOM results, and again, no significant between-group difference was found (fallers and non-fallers). Visually, this result can be confirmed by the radar graphs, which show a marked similarity between the participant groups. This result corroborates the topographic and quantization errors found by SOM, confirming the absence of a significant difference between the two groups initially considered in this study based on the self-report of just one fall episode in the 12 months before data collection. Corroborating these results, older adults with self-reports of only one fall in the last year did not exhibit significantly different patterns from those without self-reported falls when considering most kinematic variables under dual-task conditions [39]. These results are reinforced by another study in which it was demonstrated that older adults who self-reported only one fall in the last 12 months did not present any differences from non-faller older adults in terms of mean values for gait speed, cadence, step length, stride time, single-leg stance time, or variability of stride time during a dual-task gait [18]. Thus, upon examining clinical and functional characteristics in this study, it was found that these individuals categorized as fallers and non-fallers do not exhibit significant clinical and functional differences that warrant their classification in this manner.