###### Abstract  
A wide range of outcomes makes identifying clinical and functional features distinguishing older persons who fall from non-fallers challenging, especially for professionals with less clinical experience. Thus, this study aimed to map a high-dimensional and complex clinical and functional dataset and determine which outcomes better discriminate older adults with and without self-reported falls. For this, clinical, functional, and cognitive outcomes of 60 community-dwelling older adults classified as fallers and non-fallers were selected based on self-report of a single fall in the last 12 months. An unsupervised intelligent algorithm (Self-Organizing Maps--SOM) was used to cluster and topographically represent the data studied. The SOM model mapped and identified two different groups (topographic error: 0.00; sensitivity: 0.77; precision: 0.42; accuracy: 0.53; F1-score: 0.55) based on self-report of a single fall. We concluded that although two distinct groups were mapped and clustered by the SOM, participants were not necessarily fallers or non-fallers. The increased cost of cognitive demands regarding a motor task (Timed Up and Go Test) and the effect of the Trail Making Test (TMT) Part B regarding TMT Part A could discriminate the functional and cognitive patterns in community-dwelling older adults. Therefore, in clinical practice, identifying patterns involving the interaction between cognition and motor skills, even in once-only faller older adults, can be an efficient approach to assessment and, consequently, to compound intervention programs to prevent falls in this population. f 202420202420242024202420242024202024202024202024202024202024202024202024202024202024202024202024202024202024202024202024202024202024202024202024202024202420202  
Several studies have tried to discover one or more clinical and functional performance outcomes to identify older adults at greater risk of falling [3; 4]. However, given the large data volume in geriatric assessment, selecting outcomes that better represent cognitive, executive, intellectual, and motor functions is challenging in clinical practice [5]. An umbrella review of instruments assessing gait, balance, and functional mobility concluded that no univariate outcome could predict the risk of falls in older adults since this event has multifactorial causes [6]. Furthermore, identifying nuances from many outcomes can be challenging, especially for professionals with less clinical experience [7]. Thus, this task becomes relevant not only to understanding the causes related to the risk of falls [6] but also to analyzing the applicability of intelligent and multivariate methods in the assessment and grouping of individuals with similar characteristics [8], i.e., clinical and functional patterns related to falls in older adults. Machine learning (ML) is at the core of the Big Data Revolution because of its ability to learn from data and provide data-driven insights, decisions, and predictions. It promises to transform how we live, work, and think by optimizing processes, empowering insight discovery, and improving decision-making [9]. In this context, several ML-based models, such as random forest (RF), logistic regression (LR), support vector machine (SVM), light gradient boosting machine (LightGBM), and extra trees (ET) have been applied to analyze functional dependency in basic activities of daily living (ADL) and instrumental activities of daily living (IADL) in middle-aged and older adults [10]. ML-based models have also been used to analyze motion, clinical, cognitive, and functional outcomes [11; 12]. Ensemble techniques, such as AdaBoost, clustering models (e.g., k-means), and SVM, have presented promising measurement properties in identifying and differentiating the functional features in community-dwelling older adults undergoing exercise protocols [12]. From a wide range of ML methods, the Self-Organizing Map (SOM) [13] is an unsupervised artificial neural network capable of reducing data with great dimensionality and complexity [14; 15].