## 2 Materials and Methods
### Methodology
#### 2.3.3 Growing Self-Organizing Maps to Detect Variants of Impairment  
The AI module of the proposed framework uses a self-structuring version of the self-organizing map (SOM) algorithm called the growing SOM (GSOM) [11]. This algorithm has a map topography that self-structures by adapting its size and shape based on the attributes and variations of the input data without being dependent on a fixed structure. The GSOM can handle the outliers and noise in data, therefore establishing its applicability for conducting unsupervised data exploration, anomaly detection, data mining, and profiling applications in multiple domains [27].  
We selected GSOM for the AI module considering three major features that aligned well with the requirements of this study. Firstly, the unsupervised learning capability of the GSOM was important due to the exploratory nature of the analysis, aiming to discover previously unknown patterns. Compared to clustering algorithms, which need a predefined number of clusters, the GSOM has the benefit of not requiring prior knowledge of the input data. Secondly, the GSOM can not only generate clusters based on similar groupings of assessment outcomes but also capture the topological relationships among the clusters with neighborhood-preserving mappings. The self-structuring ability of the GSOM has been shown to generate clusters that better preserve the relationships in the input variables compared to the SOM [28]; therefore, GSOM was more suited for this study as we used inputs from multiple assessments. Thirdly, the GSOM 'grows' nodes starting from an initial 4-node network and, as such, is better suited for studies where multiple maps are visualized and compared. The unsupervised clustering of multiple dimensions of participant data was formed using an improved variant [29; 30] of the GSOM, with a transience mechanism facilitating the encapsulation of plasticity in the GSOM. This enabled the algorithm to discard outdated information and overfitting knowledge in its knowledge acquisition, without the loss of stability of the algorithm.  
In the GSOM, as input data are presented, nodes of the network compete with each other for ownership of the input, and the winners strengthen their relationships with this input. The competitive learning process is repeated for the complete dataset for several cycles that ultimately the maps associated output nodes with the patterns in the input dataset. The growth of the GSOM is determined by the number of dimensions in the input space and the spread factor (SF), which controls the spread of the neural network structure independent of the dimensionality (features) of the dataset [12]. To select the best hyperparameters, such as the spread factor and learning rate, a randomized approach was employed to determine the optimal values. For the spread factor, a range of 0.3 to 0.9 was evaluated, while for the learning rate, a range of 0.01 to 0.5 was considered. Usingthe GridSearch method, the optimal values were identified as a spread factor of 0.8 and a learning rate of 0.3 for this study. In this work, we utilized the Python implementation of the GSOM. As the inputs to the GSOM, we used the standardized scores of the assessments for survivors of mild stroke.