## 6 Use Case: Privacy Identity Management Enrollment
### Machine Learning  
A significant part of our application is using machine learning on each child's game scores in order to predict a risk indicator that showcases the probability of demonstrating a communication disorder. The machine learning approach followed to address this task is presented in this section. The game scores dataset we obtained from the mobile app specifically focuses on addressing ADHD deficiencies in children. The dataset contains data from 451 cases. Each entry in the dataset includes the following details:  
* **case_id:** An anonymized identifier used to distinguish cases. * **client_id:** Identifies the client to which the case belongs, useful for dataset splitting in federated settings. * **A series of scores measuring specific communication skills:** These scores, such as Verbalization, Voicing, Syntax, etc., are derived from the child's performance in specialized gamified exercises and have been computed with the assistance of expert clinicians. * **target:** Can be \(-1\) (no clinician's diagnosis available for the case), 0 (no diagnosed deficiency in the case), 1 (indicates a positive diagnosis of communication deficiency by a clinician). The machine learning task associated with this dataset involves predicting the probability of a case being diagnosed with a specific communication deficiency. We have five clinicians with 451 total cases. The dataset train-test split was performed based on our proposed solution architecture that handles this problem as an anomaly detection task using a semi-supervised learning approach. Our methodology involves training ML models on \"normal\" cases labeled as 0 (no diagnosed deficiency) and some selected unlabeled cases (\(-1\)) that are presumed to be normal. During training, the model is evaluated on all possible labels, i.e., negative, unknown, and positive cases. To this end, we utilized AutoEncoder-based architecture, a type of neural network that tries to reconstruct the original input. The goal throughout the project was to train the model on \"normal\" data and learn a hidden representation of the normal distribution. When the network encounters data that significantly differs from the training samples (in the project's context, cases with disability), we expect that it will result in a higher reconstruction error since the associated patterns differ from what the model learned as normal. During inference, when the model encounters samples that are hard to reconstruct accurately, it will result in a high reconstruction error, indicating a potential disability. In the Figures 3 and 4, there is a TSNE visualization for the whole dataset, as well as the visualization for each respective clinician participating in the process. The TSNE visualizations suggest that the cases are not easily distinguishable in the reduced feature space. In both instances, a significant overlap between the different labels is observed. In addition, the clusters do not show a clear separation, which implies that there is not a straightforward linear boundary that separates a negative and a positive deficiency diagnosis. Figure 3: TSNE visualization of the dataset. As already mentioned, the model that we are going to utilize in our application is an AutoEncoder. An AutoEncoder is a type of machine learning model that learns to compress data into a reduced hidden representation and then reconstructs the original data as accurately as possible from this compressed form. It consists of an encoder that transforms the input into a latent space and a decoder that reconstructs the input from this representation. This characteristic makes AutoEncoders particularly suitable for our task, where the model is trained in normal cases. The model learns a hidden representation of these normal cases, and we expect a low reconstruction error for negative diagnoses, enabling the detection of positive instances by highlighting them with a higher reconstruction error.